Height analysis

# install.packages("zoo")
library(dplyr)
library(ggplot2)
library(lubridate)   
library(hydroGOF)
library(xtable)
library(knitr)
library(tidyr)
library(RSQLite)
library(agricolae)
library(scales)
library(zoo)
library(lme4)

lode observed data

upDir <- "D:/R/CombinedData/"
obsData <- "D:/R/CombinedData/"
obsAll <- read.table(paste0(obsData, "ObsAll.txt"),
                   header = TRUE)
obsA<- obsAll %>%
  mutate(StartDate=dmy(StartDate),MidDate=dmy(MidDate),FinishDate=dmy(FinishDate),Clock.Today=dmy(Clock.Today)) %>%
  #mutate(SowingDate=as.factor(ifelse(SowingDate=="no","Sd_No",paste0("Sd_",SowingDate)))) %>% # assume this is typo to be fixed?
  mutate(GrowthSeason1=as.factor(paste0("Gs_",GrowthSeason))) %>% # creating more intuitive labels here
  mutate(Rotation1=as.factor(paste0("Rt_",Rotation))) %>%
  mutate(ExpUnitCode=as.factor(paste0(Name,GrowthSeason1,Rotation1))) %>%
  mutate(Clock.Today1 = as.POSIXct(paste(Clock.Today,Time),format="%Y-%m-%d %H:%M:%S"))
  
summary(obsA)
  Clock.Today                                    Name          Collection         Experiment   Water      Defoliation
 Min.   :1997-01-28   Iversen_91DefoliationLL      : 834   1997_2001: 961   Lincoln2010:2513   dry:3041   HH: 288    
 1st Qu.:2001-08-30   Iversen_121DefoliationLLFDFD5: 734   2000_2002:1070   Lincoln2000:1222   irr:3428   LL:6181    
 Median :2011-04-30   Iversen_8Waterirr            : 500   2002_2004: 834   Lincoln2015:1089                         
 Mean   :2008-01-24   Iversen_8Waterdry            : 461   2010_2012:2513   Lincoln2001: 387                         
 3rd Qu.:2012-04-04   Iversen_12SowingDateSD1      : 425   2014_2018:1090   Lincoln2003: 365                         
 Max.   :2018-01-15   Iversen_12SowingDateSD2      : 390   2014_2019:   1   Lincoln2002: 247                         
                      (Other)                      :3125                    (Other)    : 646                         
   SowingDate      FD        GrowthSeason     Rotation       StartDate             MidDate             FinishDate        
 No     :1795   FD10:  35   Min.   :1.00   Min.   :1.000   Min.   :1996-11-01   Min.   :1996-12-27   Min.   :1997-02-21  
 no     :1091   FD2 :  34   1st Qu.:1.00   1st Qu.:1.000   1st Qu.:2001-07-03   1st Qu.:2001-08-16   1st Qu.:2001-09-27  
 SD1    : 961   FD5 :6400   Median :2.00   Median :3.000   Median :2011-03-11   Median :2011-04-11   Median :2011-05-17  
 SD2    : 592               Mean   :1.99   Mean   :2.964   Mean   :2007-12-17   Mean   :2008-01-17   Mean   :2008-02-18  
 SD3    : 545               3rd Qu.:2.00   3rd Qu.:4.000   3rd Qu.:2012-02-28   3rd Qu.:2012-04-02   3rd Qu.:2012-05-01  
 SD4    : 488               Max.   :6.00   Max.   :7.000   Max.   :2017-12-04   Max.   :2017-12-25   Max.   :2018-01-15  
 (Other): 997                                                                                                            
    Interval              Variable      VariableUnits        Time         Observed           StdDEV       GrowthSeason1
 Min.   :  0.00   NodeNumber  :1297   %        : 220   12:00:00:6469   Min.   :    0.0   Min.   :   0.0   Gs_1:2537    
 1st Qu.: 20.00   shootbiomass: 980   cm       : 454                   1st Qu.:    4.2   1st Qu.:   0.1   Gs_2:2690    
 Median : 33.00   LAI         : 924   fractio0l:1958                   Median :   13.7   Median :   0.6   Gs_3: 515    
 Mean   : 37.96   Height      : 697   Fraction : 434                   Mean   : 1019.3   Mean   : 110.0   Gs_4: 276    
 3rd Qu.: 51.00   SWC         : 575   kg/ha    :1661                   3rd Qu.:  694.8   3rd Qu.:  73.5   Gs_5: 400    
 Max.   :125.00   TotalLeafNo : 382   m2/m2    : 924                   Max.   :16554.5   Max.   :7307.6   Gs_6:  51    
                  (Other)     :1614   mm       : 818                                     NA's   :1192                  
 Rotation1                             ExpUnitCode    Clock.Today1                
 Rt_1:1891   Iversen_91DefoliationLLGs_2Rt_1 :  80   Min.   :1997-01-28 12:00:00  
 Rt_2:1303   Iversen_12SowingDateSD9Gs_1Rt_1 :  75   1st Qu.:2001-08-30 12:00:00  
 Rt_3:1032   Iversen_91DefoliationLLGs_1Rt_1 :  67   Median :2011-04-30 12:00:00  
 Rt_4: 745   Iversen_12SowingDateSD8Gs_1Rt_1 :  66   Mean   :2008-01-24 23:25:59  
 Rt_5: 663   Iversen_12SowingDateSD6Gs_1Rt_1 :  64   3rd Qu.:2012-04-04 12:00:00  
 Rt_6: 558   Iversen_12SowingDateSD10Gs_1Rt_1:  63   Max.   :2018-01-15 12:00:00  
 Rt_7: 277   (Other)                         :6054                                
obsA

Load Tt and Join Observed data together

upDir <- "D:/R/"
obsData <- "D:/R/TtAll/"
Tt<- read.table(paste0(obsData, "df.all.txt"),
               header = TRUE)
TtA <- Tt %>% mutate(Clock.Today=dmy(Clock.Today), ExpUnitCode=as.factor(ExpName))
TtA
ObsH <-merge(obsA,TtA,by=c("Clock.Today","ExpUnitCode")) %>%
  mutate(GrowthRotation=as.factor(paste0(GrowthSeason.x,Rotation.x)))%>%
  dplyr::filter(Water.x=="irr")%>%
  dplyr::filter(Defoliation.x=="LL")%>%
  dplyr::filter(Variable=="Height")%>%
  dplyr::filter(Tb==1)
  
summary(ObsH)
  Clock.Today                                         ExpUnitCode                              Name         Collection 
 Min.   :1997-10-23   Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14   Iversen_121DefoliationLLFDFD5 :108   1997_2001: 68  
 1st Qu.:2001-02-20   Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12   Iversen_91DefoliationLL       :107   2000_2002:125  
 Median :2002-10-25   Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11   Iversen_8Waterirr             : 68   2002_2004:107  
 Mean   :2005-09-17   Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11   Iversen_9SowingDateSD1Waterirr: 67   2010_2012:  0  
 3rd Qu.:2015-02-09   Iversen_8WaterirrGs_5Rt_1             : 10   Iversen_9SowingDateSD2Waterirr: 25   2014_2018:108  
 Max.   :2018-01-15   Iversen_91DefoliationLLGs_2Rt_1       : 10   Iversen_9SowingDateSD3Waterirr: 18   2014_2019:  0  
                      (Other)                               :340   (Other)                       : 15                  
      Experiment.x Water.x   Defoliation.x  SowingDate.x   FD.x     GrowthSeason.x    Rotation.x      StartDate         
 Lincoln2000:131   dry:  0   HH:  0        No     :175   FD10:  0   Min.   :1.000   Min.   :1.000   Min.   :1997-10-07  
 Lincoln2015:108   irr:408   LL:408        no     :108   FD2 :  0   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:2001-01-24  
 Lincoln2003: 54                           SD1    : 67   FD5 :408   Median :2.000   Median :3.000   Median :2002-10-06  
 Lincoln2001: 35                           SD2    : 25              Mean   :2.105   Mean   :3.098   Mean   :2005-08-16  
 Lincoln2004: 31                           SD3    : 18              3rd Qu.:3.000   3rd Qu.:5.000   3rd Qu.:2015-01-30  
 Lincoln2002: 22                           SD4    : 15              Max.   :5.000   Max.   :7.000   Max.   :2017-12-04  
 (Other)    : 27                           (Other):  0                                                                  
    MidDate             FinishDate            Interval            Variable     VariableUnits       Time    
 Min.   :1997-10-28   Min.   :1997-11-19   Min.   :  0.00   Height    :408   %        :  0   12:00:00:408  
 1st Qu.:2001-02-13   1st Qu.:2001-03-23   1st Qu.: 14.75   Branch    :  0   cm       :233                 
 Median :2002-10-26   Median :2002-11-16   Median : 28.00   Fraction  :  0   fractio0l:  0                 
 Mean   :2005-09-14   Mean   :2005-10-14   Mean   : 32.18   HardStemWt:  0   Fraction :  0                 
 3rd Qu.:2015-02-19   3rd Qu.:2015-03-11   3rd Qu.: 41.00   LAI       :  0   kg/ha    :  0                 
 Max.   :2017-12-25   Max.   :2018-01-15   Max.   :116.00   LeafWt    :  0   m2/m2    :  0                 
                                                            (Other)   :  0   mm       :175                 
    Observed          StdDEV       GrowthSeason1 Rotation1   Clock.Today1                      year           day       
 Min.   :  0.00   Min.   : 0.000   Gs_1:160      Rt_1:112   Min.   :1997-10-23 12:00:00   Min.   :1997   Min.   :  1.0  
 1st Qu.: 10.17   1st Qu.: 0.000   Gs_2:140      Rt_2: 80   1st Qu.:2001-02-20 12:00:00   1st Qu.:2001   1st Qu.: 63.0  
 Median : 35.62   Median : 3.107   Gs_3: 54      Rt_3: 59   Median :2002-10-25 12:00:00   Median :2002   Median :138.5  
 Mean   :107.19   Mean   :12.001   Gs_4: 13      Rt_4: 50   Mean   :2005-09-17 22:25:44   Mean   :2005   Mean   :170.4  
 3rd Qu.:129.12   3rd Qu.:16.955   Gs_5: 41      Rt_5: 46   3rd Qu.:2015-02-10 06:00:00   3rd Qu.:2015   3rd Qu.:292.0  
 Max.   :681.30   Max.   :91.520   Gs_6:  0      Rt_6: 42   Max.   :2018-01-15 12:00:00   Max.   :2018   Max.   :365.0  
                  NA's   :229                    Rt_7: 19                                                               
      rain             maxt            mint             mean            radn            wind             vp       
 Min.   : 0.000   Min.   : 7.90   Min.   :-4.900   Min.   : 2.55   Min.   : 1.50   Min.   :0.700   Min.   : 5.10  
 1st Qu.: 0.000   1st Qu.:15.32   1st Qu.: 4.375   1st Qu.:10.40   1st Qu.: 9.70   1st Qu.:2.900   1st Qu.:10.00  
 Median : 0.000   Median :19.00   Median : 8.700   Median :13.20   Median :16.14   Median :3.900   Median :11.40  
 Mean   : 0.876   Mean   :19.17   Mean   : 7.853   Mean   :13.49   Mean   :16.73   Mean   :3.997   Mean   :11.77  
 3rd Qu.: 0.000   3rd Qu.:22.23   3rd Qu.:11.300   3rd Qu.:16.50   3rd Qu.:22.62   3rd Qu.:4.900   3rd Qu.:13.72  
 Max.   :31.800   Max.   :33.80   Max.   :20.600   Max.   :26.20   Max.   :33.40   Max.   :9.300   Max.   :22.00  
                                                                                                                  
       Pp              Tb        TTbeta            Tbb       TTbroken           TbF        TTfick      
 Min.   :10.02   Min.   :1   Min.   : 0.128   Min.   :1   Min.   : 1.841   Min.   :1   Min.   : 2.222  
 1st Qu.:12.21   1st Qu.:1   1st Qu.: 2.247   1st Qu.:1   1st Qu.: 6.822   1st Qu.:1   1st Qu.: 8.172  
 Median :14.27   Median :1   Median : 4.118   Median :1   Median : 9.015   Median :1   Median :10.646  
 Mean   :13.93   Mean   :1   Mean   : 5.812   Mean   :1   Mean   : 9.535   Mean   :1   Mean   :10.878  
 3rd Qu.:15.82   3rd Qu.:1   3rd Qu.: 8.085   3rd Qu.:1   3rd Qu.:12.040   3rd Qu.:1   3rd Qu.:13.588  
 Max.   :16.65   Max.   :1   Max.   :23.343   Max.   :1   Max.   :20.258   Max.   :1   Max.   :20.813  
                                                                                                       
                                   ExpName         Experiment.y Water.y   Defoliation.y  SowingDate.y   FD.y    
 Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14   Lincoln1997: 68   dry:  0   HH:  0        No     :175   FD10:  0  
 Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12   Lincoln2000:110   irr:408   LL:408        no     :108   FD2 :  0  
 Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11   Lincoln2001: 15             LS:  0        SD1    : 67   FD5 :408  
 Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11   Lincoln2002:107             SL:  0        SD2    : 25             
 Iversen_8WaterirrGs_5Rt_1             : 10   Lincoln2010:  0             SS:  0        SD3    : 18             
 Iversen_91DefoliationLLGs_2Rt_1       : 10   Lincoln2015:108                           SD4    : 15             
 (Other)                               :340                                             (Other):  0             
 GrowthSeason.y   Rotation.y   Tt_beta_sum        Tt_fick_sum       Tt_broken_sum           Ppm            Tmean       
 Gs_1:160       Rt_1   :112   Min.   :  0.2834   Min.   :   4.022   Min.   :   3.333   Min.   :10.25   Min.   : 7.024  
 Gs_2:140       Rt_2   : 80   1st Qu.: 60.8023   1st Qu.: 165.210   1st Qu.: 142.690   1st Qu.:11.99   1st Qu.:10.974  
 Gs_3: 54       Rt_3   : 59   Median :123.4401   Median : 299.224   Median : 258.979   Median :14.48   Median :13.963  
 Gs_4: 13       Rt_4   : 50   Mean   :157.2934   Mean   : 326.606   Mean   : 283.618   Mean   :13.84   Mean   :13.226  
 Gs_5: 41       Rt_5   : 46   3rd Qu.:218.2569   3rd Qu.: 453.159   3rd Qu.: 395.238   3rd Qu.:15.99   3rd Qu.:15.771  
 Gs_6:  0       Rt_6   : 42   Max.   :722.6708   Max.   :1186.654   Max.   :1051.876   Max.   :16.55   Max.   :19.327  
                (Other): 19                                                                                            
 GrowthRotation
 11     : 60   
 12     : 38   
 21     : 24   
 26     : 24   
 13     : 23   
 22     : 23   
 (Other):216   
obsheight<-ObsH%>%
  dplyr::filter(Name=="Iversen_8Waterirr")
obsheight%>%
  ggplot(aes(x=Tt_broken_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time(°Cd)")+ylab("Plant height (mm)")+
  geom_smooth(method = "lm", se = TRUE,linetype=1 , colour="black")+
 facet_grid(GrowthSeason.x~Rotation.x)+ggtitle("Iversen_8Waterirr")+
  theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
 theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

Iversen_91DefoliationLL

obsheight1<-ObsH%>%
  dplyr::filter(Name=="Iversen_91DefoliationLL")
obsheight1%>%
  ggplot(aes(x=Tt_broken_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time(°Cd)")+ylab("Plant height (mm)")+ggtitle("Iversen_91DefoliationLL")+
 facet_grid(GrowthSeason.x~Rotation.x)+
  geom_smooth(method = "lm", se = TRUE,linetype=1 , colour="black")+
  theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
 theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

NA

Iversen_9SowingDateSD1Waterirr

obsheight3<-ObsH%>%
  dplyr::filter(Name=="Iversen_9SowingDateSD1Waterirr")%>%
  mutate(Observed=Observed*10)
 
obsheight3%>%
  ggplot(aes(x=Tt_broken_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time(°Cd)")+ylab("Plant height (mm)")+ggtitle("Iversen_9SowingDateSD1Waterirr")+
 facet_grid(GrowthSeason.x~Rotation.x)+
  geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="black")+
  theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
 theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

NA

Iversen_9SowingDateSD2Waterirr

obsheight1<-ObsH%>%
  dplyr::filter(Name=="Iversen_9SowingDateSD2Waterirr")%>%
  mutate(Observed=Observed*10)
 
obsheight1
obsheight1%>%
  ggplot(aes(x=Tt_broken_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time(°Cd)")+ylab("Plant height (mm)")+ggtitle("Iversen_9SowingDateSD2Waterirr")+
 facet_grid(GrowthSeason.x~Rotation.x)+
  geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="black")+
  theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
 theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

NA

Iversen_9SowingDateSD3Waterirr

obsheight1<-ObsH%>%
  dplyr::filter(Name=="Iversen_9SowingDateSD3Waterirr")%>%
  mutate(Observed=Observed*10)
 
obsheight1
obsheight1%>%
  ggplot(aes(x=Tt_broken_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time(°Cd)")+ylab("Plant height (mm)")+ggtitle("Iversen_9SowingDateSD3Waterirr")+
 facet_grid(GrowthSeason.x~Rotation.x)+
  geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="black")+
  theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
 theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

NA

Iversen_9SowingDateSD4Waterirr

obsheight1<-ObsH%>%
  dplyr::filter(Name=="Iversen_9SowingDateSD4Waterirr")%>%
  mutate(Observed=Observed*10)
 
obsheight1
obsheight1%>%
  ggplot(aes(x=Tt_broken_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time(°Cd)")+ylab("Plant height (mm)")+ggtitle("Iversen_9SowingDateSD4Waterirr")+
 facet_grid(GrowthSeason.x~Rotation.x)+
  geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="black")+
  theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
 theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

NA

Iverson12DefoliationFD5

obsheight1<-ObsH%>%
  dplyr::filter(Name=="Iversen_121DefoliationLLFDFD5")%>%
  mutate(Observed=Observed*10)
 
obsheight1%>%
  ggplot(aes(x=Tt_broken_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time(°Cd)")+ylab("Plant height (mm)")+ggtitle("Iversen_121DefoliationLLFDFD5")+
 facet_grid(GrowthSeason.x~Rotation.x)+
  geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="black")+
  theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
 theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

NA

convert unit

calculate for heightchron

obsH2<-ObsH%>%
  dplyr::filter(Collection!="1997_2001")%>%
  dplyr::filter(Collection!="2002_2004")%>%
  mutate(Observed=Observed*10)
obsH3<-ObsH%>%
  dplyr::filter(Collection!="2000_2002")%>%
  dplyr::filter(Collection!="2014_2018")
obsH3
obsHN<-rbind(obsH2,obsH3)
obsHN
obsSlope <- obsHN%>%
  group_by(Name,GrowthSeason.x,Rotation.x,Collection,Tmean,Ppm,GrowthRotation) %>%
  do(mod = lm(Tt_broken_sum~Observed,data=.)) %>%
  mutate(slope = summary(mod)$coeff[2]) %>%
  dplyr::select(-mod)
obsSlope

load Rotation and Growth season

phyll <- "D:\\R\\"
StartGrazing <- read.table(paste0(phyll, "ExperimentList.txt"), 
                      header = TRUE)
StartGrazing1<-StartGrazing %>%
  mutate(GrowthRotation= as.factor(paste0(GrowthSeason,Rotation)))
HchronPp<- merge(StartGrazing1,obsSlope,by=c("Name","Collection","GrowthRotation"))
HchronPp1<-HchronPp%>%
dplyr::filter(Name!="Iversen_8Waterirr"|GrowthRotation!="57")%>%
dplyr::filter(Name!="Iversen_121DefoliationLLFDFD5"|GrowthRotation!="36")%>%
 dplyr::filter(Name!="Iversen_121DefoliationLLFDFD5"|GrowthRotation!="14")%>%
 dplyr::filter(Name!="Iversen_121DefoliationLLFDFD5"|GrowthRotation!="37")%>%
 dplyr::filter(Name!="Iversen_121DefoliationLLFDFD5"|GrowthRotation!="12")%>%
 dplyr::filter(Name!="Iversen_121DefoliationLLFDFD5"|GrowthRotation!="41")%>%
 dplyr::filter(Name!="Iversen_8Waterirr"|GrowthRotation!="26")%>%
 dplyr::filter(Name!="Iversen_8Waterirr"|GrowthRotation!="32")%>%
 dplyr::filter(Name!="Iversen_91DefoliationLL"|GrowthRotation!="27")%>%
 dplyr::filter(Name!="Iversen_91DefoliationLL"|GrowthRotation!="17")%>%
 dplyr::filter(Name!="Iversen_91DefoliationLL"|GrowthRotation!="11")%>%
 #   # dplyr::filter(Name!="Iversen_91DefoliationLL"|GrowthRotation!="17")%>%
 dplyr::filter(Name!="Iversen_9SowingDateSD1Waterirr"|GrowthRotation!="14")%>%
 dplyr::filter(Name!="Iversen_9SowingDateSD1Waterirr"|GrowthRotation!="21")%>%
 #   # dplyr::filter(Name!="Iversen_9SowingDateSD1Waterirr"|GrowthRotation!="26")%>%
 dplyr::filter(Name!="Iversen_9SowingDateSD2Waterirr"|GrowthRotation!="13")
 ##dplyr::filter(Name!="Iversen_9SowingDateSD4Waterirr"|GrowthRotation!="13")%>%
 #dplyr::filter(Stage!="Seedling")
HchronPp1%>%
  ggplot(aes(x=Ppm, y=slope, colour=factor(Name),label=GrowthRotation))+geom_text()+theme_bw()+xlab("Mean photoperiod (h)")+ylab(" Heightchron (°Cd/mm)")+
  geom_smooth(method = "lm", se = TRUE, formula=y ~ poly(x, 2, raw=TRUE), colour="darkgrey")+
  facet_wrap(~Stage,ncol = 2)+theme(legend.title = element_blank())+
   theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
 theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

HchronPp
X<-HchronPp1$Ppm
Y<-HchronPp1$slope
Xsq<-X^2
Xcub<-X^3
plot(X,Y, pch=19)
model1<-lm(Y~X)
model2<-lm(Y~X+Xsq)
model3<-lm(Y~X+Xsq+Xcub)
mod_lm <-lm(Y~X*(X<14.2)+X*(X>=14.2),data=HchronPp)
anova(model1)
Analysis of Variance Table

Response: Y
          Df Sum Sq Mean Sq F value   Pr(>F)    
X          1 5.3993  5.3993  52.073 2.48e-09 ***
Residuals 51 5.2881  0.1037                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
summary(model1)

Call:
lm(formula = Y ~ X)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.42533 -0.19184 -0.09654  0.07362  0.92300 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  3.56353    0.36014   9.895 1.85e-13 ***
X           -0.17883    0.02478  -7.216 2.48e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.322 on 51 degrees of freedom
Multiple R-squared:  0.5052,    Adjusted R-squared:  0.4955 
F-statistic: 52.07 on 1 and 51 DF,  p-value: 2.48e-09
anova(model2)
Analysis of Variance Table

Response: Y
          Df Sum Sq Mean Sq F value    Pr(>F)    
X          1 5.3993  5.3993  67.150 8.334e-11 ***
Xsq        1 1.2677  1.2677  15.766 0.0002297 ***
Residuals 50 4.0204  0.0804                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
summary(model2)

Call:
lm(formula = Y ~ X + Xsq)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.32871 -0.19718 -0.06290  0.04647  0.82851 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 15.33706    2.98204   5.143 4.53e-06 ***
X           -1.89493    0.43275  -4.379 6.09e-05 ***
Xsq          0.06145    0.01547   3.971  0.00023 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2836 on 50 degrees of freedom
Multiple R-squared:  0.6238,    Adjusted R-squared:  0.6088 
F-statistic: 41.46 on 2 and 50 DF,  p-value: 2.426e-11
anova(model3)
Analysis of Variance Table

Response: Y
          Df Sum Sq Mean Sq F value    Pr(>F)    
X          1 5.3993  5.3993 72.2337  3.33e-11 ***
Xsq        1 1.2677  1.2677 16.9599 0.0001462 ***
Xcub       1 0.3577  0.3577  4.7856 0.0334994 *  
Residuals 49 3.6627  0.0747                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
summary(model3)

Call:
lm(formula = Y ~ X + Xsq + Xcub)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.35197 -0.15505 -0.05562  0.04544  0.83225 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  73.6600    26.8153   2.747  0.00839 **
X           -14.7186     5.8768  -2.505  0.01564 * 
Xsq           0.9921     0.4257   2.331  0.02393 * 
Xcub         -0.0223     0.0102  -2.188  0.03350 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2734 on 49 degrees of freedom
Multiple R-squared:  0.6573,    Adjusted R-squared:  0.6363 
F-statistic: 31.33 on 3 and 49 DF,  p-value: 1.873e-11
abline(model1, col="red")
XV<-seq(min(X),max(X),0.01)
yv<-predict(model2,list(X=XV,Xsq=XV^2))
lines(XV,yv,col="blue")

NA
NA

Fit a polynomial regression model

```{r}

HchronPp1

X<-HchronPp1\(Pmean # Y<-HchronPp1\)slope

Xsq<-X^2

Xcub<-X^3

plot(X,Y, pch=19)

model1<-lm(Y~X)

model2<-lm(Y~X+Xsq)

model3<-lm(Y~X+Xsq+Xcub)

mod_lm <-lm(Y~X(X<14.2)+X(X>=14.2),data=HchronPp1)

anova(model1)

summary(model1)

anova(model2)

summary(model2)

anova(model3)

summary(model3)

abline(model1, col=“red”)

XV<-seq(min(HchronPp1\(Pmean),max(HchronPp1\)Pmean),0.01)

yv<-predict(model2,list(X=XV,Xsq=XV^2))

lines(XV,yv,col=“blue”)

title(xlab=“Mean photoperiod (h)”,ylab=“Heightchron (mm/cd )”)

Define stats function

  • Using Gauch et al. 2003 (Model evaluation by comparison of model-based predictions and measured values. Agron. J. 95, 1442-1446)

Test stats functions used

s <- c(4231.972,3935.604,3779.652,3627.687,3363.499,3230.566,2868.114,2868.827)
m <- c(4987.66,5636.09,4754.06,4114.53,4141.72,3704.06,5142.19,4762.03)
x <- gauchStats(s,m)
tempDf <- data.frame(statName=c("SB","NU","LC","r_MSD","R2"), statValue=x)
# kable(tempDf, digits= 2)
tempDf2 <- data.frame(Predicted=s, Observed=m)
x <- tempDf2 %>%
  summarise(
    n = n(),
    r2 = gauchStats(Predicted,Observed)[5],
  #  rmse = round(rmse(Predicted,Observed),0),
    r_rmse = round(rmse(Predicted,Observed)/mean(Observed)*100,1),
    nse = round(NSE(Predicted,Observed),1),
    sb = gauchStats(Predicted,Observed)[1],
  nu = gauchStats(Predicted,Observed)[2],
  lc = gauchStats(Predicted,Observed)[3]
  ) %>% 
  t() 
df <- data.frame(stat = row.names(x),statvalue = x[,1])
df %>%
  kable(format = "markdown")
stat statvalue
n n 8.0
r2 r2 7.1
r_rmse r_rmse 28.7
nse nse -4.1
sb sb 76.5
nu nu 5.2
lc lc 18.2

Load simulated database

create function to read data (Justin’s script)

load address of db

set table to be enquierd

load table into an object

make it a dataframe

change date to corerct format

explore the df

db.address <- "D:\\APSIMX2\\Prototypes\\Lucerne\\LucerneValidation.db"
tableName<-"Report"
DbTable <- GetApsimNGTable(db.address,tableName)
df <- as.data.frame(DbTable)
df$Clock.Today <- ymd_hms(df$Clock.Today)
str(df)
'data.frame':   56703 obs. of  234 variables:
 $ SimulationID                                       : int  1 2 1 2 1 2 1 2 1 2 ...
 $ Water                                              : chr  "dry" "irr" "dry" "irr" ...
 $ Zone                                               : chr  "paddock" "paddock" "paddock" "paddock" ...
 $ Clock.Today                                        : POSIXct, format: "1996-10-31 12:00:00" "1996-10-31 12:00:00" "1996-11-01 12:00:00" "1996-11-01 12:00:00" ...
 $ DiagnosticsVariables.Script.AccumPlantN            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DiagnosticsVariables.Script.AccumMineralisation    : num  0 0 -0.00561 -0.00561 -0.00699 ...
 $ DiagnosticsVariables.Script.AccumDenit             : num  0 0 -0.184 -0.184 -0.254 ...
 $ DiagnosticsVariables.Script.AccumFert              : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DiagnosticsVariables.Script.AccumLeach             : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DiagnosticsVariables.Script.AccumDetach            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DiagnosticsVariables.Script.DeltaSoilOMN           : num  0 0 0.00561 0.00561 0.00699 ...
 $ DiagnosticsVariables.Script.DeltaSurfaceOMN        : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DiagnosticsVariables.Script.DeltaSoilMineralN      : num  0 0 -0.189 -0.189 -0.261 ...
 $ Lucerne.Root.NSupply.Fixation                      : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Lucerne.Root.NSupply.Reallocation                  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Lucerne.Root.NSupply.Retranslocation               : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Lucerne.Root.NSupply.Uptake                        : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Soil.SoilWater.Eo                                  : num  1.89 1.89 4.13 4.13 4.58 ...
 $ Soil.SoilWater.Es                                  : num  1.89 1.89 4.13 4.13 3.74 ...
 $ SWC                                                : num  752 752 749 749 746 ...
 $ DiagnosticsVariables.Script.DUL                    : num  744 744 744 744 744 744 744 744 744 744 ...
 $ Soil.SoilWater.Drainage                            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Soil.SoilWater.Runoff                              : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DiagnosticsVariables.Script.OutFlowLat             : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DiagnosticsVariables.Script.AccumEO                : num  -1.89 -1.89 -6.02 -6.02 -10.6 ...
 $ DiagnosticsVariables.Script.AccumEP                : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DiagnosticsVariables.Script.AccumES                : num  -1.89 -1.89 -6.02 -6.02 -9.76 ...
 $ DiagnosticsVariables.Script.AccumDrainage          : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DiagnosticsVariables.Script.AccumRunoff            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DiagnosticsVariables.Script.AccumRainfall          : num  10.4 10.4 12.2 12.2 12.9 ...
 $ DiagnosticsVariables.Script.AccumIrrigation        : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DiagnosticsVariables.Script.AccumOutflowLat        : num  0 0 0 0 0 0 0 0 0 0 ...
 $ DiagnosticsVariables.Script.SoilWaterDeficit       : num  0 0 7.63 7.63 5.3 ...
 $ Lucerne.Grain.Live.Wt                              : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Lucerne.Shell.Live.Wt                              : num  0 0 0 0 0 0 0 0 0 0 ...
 $ StemWt                                             : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Lucerne.Stem.Live.Wt                               : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Lucerne.Grain.Live.N                               : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Lucerne.Shell.Live.N                               : num  0 0 0 0 0 0 0 0 0 0 ...
 $ LeafWt                                             : num  0 0 0 0 0 0 0 0 0 0 ...
 $ RootWt                                             : num  900 900 900 900 900 900 900 900 900 900 ...
 $ Lucerne.Leaf.Live.N                                : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Lucerne.Root.Live.N                                : num  0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 ...
 $ Lucerne.Leaf.Live.NConc                            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Lucerne.Root.Live.NConc                            : num  0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ...
 $ Lucerne.Root.WaterUptake                           : num  0 0 0 0 0 0 0 0 0 0 ...
 $ ET                                                 : num  1.89 1.89 4.13 4.13 3.74 ...
 $ Lucerne.Root.Depth                                 : num  20 20 23 23 26 26 29 29 32 32 ...
 $ Lucerne.Leaf.CoverTotal                            : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Lucerne.Leaf.CoverDead                             : num  0 0 0 0 0 0 0 0 0 0 ...
 $ LAI                                                : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Height                                             : num  0 0 0 0 0 0 0 0 0 0 ...
 $ SWmm.1.                                            : num  35 35 33.6 33.6 32.1 ...
 $ SWmm.2.                                            : num  35.8 35.8 33.8 33.8 32.5 ...
 $ SWmm.3.                                            : num  35.1 35.1 33.9 33.9 32.7 ...
 $ SWmm.4.                                            : num  30.3 30.3 30.3 30.3 30.1 ...
 $ SWmm.5.                                            : num  28.4 28.4 28.4 28.4 28.2 ...
 $ SWmm.6.                                            : num  30.8 30.8 30.8 30.8 30.8 ...
 $ SWmm.7.                                            : num  32.4 32.4 32.4 32.4 32.4 ...
 $ SWmm.8.                                            : num  33.4 33.4 33.4 33.4 33.4 ...
 $ SWmm.9.                                            : num  33.6 33.6 33.6 33.6 33.6 ...
 $ SWmm.10.                                           : num  34.5 34.5 34.5 34.5 34.5 ...
 $ SWmm.11.                                           : num  34.5 34.5 34.5 34.5 34.5 ...
 $ SWmm.12.                                           : num  34.5 34.5 34.5 34.5 34.5 ...
 $ SWmm.13.                                           : num  34.5 34.5 34.5 34.5 34.5 ...
 $ SWmm.14.                                           : num  33.9 33.9 33.9 33.9 33.9 ...
 $ SWmm.15.                                           : num  33.6 33.6 33.9 33.9 33.9 ...
 $ SWmm.16.                                           : num  32.7 32.7 34 34 34 ...
 $ SWmm.17.                                           : num  30 30 30.6 30.6 32.1 ...
 $ SWmm.18.                                           : num  30 30 30 30 30 ...
 $ SWmm.19.                                           : num  30 30 30 30 30 30 30 30 30 30 ...
 $ SWmm.20.                                           : num  30.9 30.9 30.9 30.9 30.9 ...
 $ SWmm.21.                                           : num  31 31 31 31 31 31 31 31 31 31 ...
 $ SWmm.22.                                           : num  32 32 32 32 32 32 32 32 32 32 ...
 $ SWmm.23.                                           : num  34.9 34.9 34.9 34.9 34.9 ...
 $ Soil.NO3N.1.                                       : num  175 175 173 173 175 ...
 $ Soil.NO3N.2.                                       : num  47.2 47.2 47.8 47.8 45.7 ...
 $ Soil.NO3N.3.                                       : num  22.2 22.2 22.1 22.1 21.3 ...
 $ Soil.NO3N.4.                                       : num  4.05 4.05 5.12 5.12 5.53 ...
 $ Soil.NO3N.5.                                       : num  1.95 1.95 2.15 2.15 2.27 ...
 $ Soil.NO3N.6.                                       : num  1.67 1.67 1.71 1.71 1.74 ...
 $ Soil.NO3N.7.                                       : num  1.64 1.64 1.64 1.64 1.64 ...
 $ Soil.NO3N.8.                                       : num  1.6 1.6 1.6 1.6 1.6 ...
 $ Soil.NO3N.9.                                       : num  1.64 1.64 1.63 1.63 1.63 ...
 $ Soil.NO3N.10.                                      : num  0.187 0.187 0.274 0.274 0.326 ...
 $ Soil.NO3N.11.                                      : num  0.0161 0.0161 0.0291 0.0291 0.0378 ...
 $ Soil.NO3N.12.                                      : num  0.000302 0.000302 0.0003 0.0003 0.000319 ...
 $ Soil.NO3N.13.                                      : num  0.000144 0.000144 0.000278 0.000278 0.000308 ...
 $ Soil.NO3N.14.                                      : num  2.25e-05 2.25e-05 4.98e-05 4.98e-05 7.36e-05 ...
 $ Soil.NO3N.15.                                      : num  1.50e-05 1.50e-05 2.88e-05 2.88e-05 4.30e-05 ...
 $ Soil.NO3N.16.                                      : num  1.6 1.6 1.56 1.56 1.49 ...
 $ Soil.NO3N.17.                                      : num  1.6 1.6 1.65 1.65 1.72 ...
 $ Soil.NO3N.18.                                      : num  1.6 1.6 1.62 1.62 1.63 ...
 $ Soil.NO3N.19.                                      : num  1.61 1.61 1.62 1.62 1.63 ...
 $ Soil.NO3N.20.                                      : num  1.6 1.6 1.62 1.62 1.63 ...
 $ Soil.NO3N.21.                                      : num  1.61 1.61 1.62 1.62 1.63 ...
 $ Soil.NO3N.22.                                      : num  1.61 1.61 1.62 1.62 1.63 ...
 $ Soil.NO3N.23.                                      : num  1.6 1.6 1.61 1.61 1.63 ...
 $ DiagnosticsVariables.Script.SoilNitrogenContent    : num  272 272 272 272 272 ...
  [list output truncated]
summary(df)
  SimulationID      Water               Zone            Clock.Today                 
 Min.   : 1.00   Length:56703       Length:56703       Min.   :1979-01-01 12:00:00  
 1st Qu.:14.00   Class :character   Class :character   1st Qu.:1998-11-20 12:00:00  
 Median :40.00   Mode  :character   Mode  :character   Median :2001-11-30 12:00:00  
 Mean   :34.87                                         Mean   :2004-04-22 12:38:26  
 DiagnosticsVariables.Script.AccumPlantN DiagnosticsVariables.Script.AccumMineralisation
 Min.   :  0.0                           Min.   : -0.1345                               
 1st Qu.:113.7                           1st Qu.: 48.0568                               
 Median :278.8                           Median : 97.2750                               
 Mean   :253.9                           Mean   :132.5251                               
 DiagnosticsVariables.Script.AccumDenit DiagnosticsVariables.Script.AccumFert DiagnosticsVariables.Script.AccumLeach
 Min.   :-120.284                       Min.   :0                             Min.   :-127.0290                     
 1st Qu.: -11.333                       1st Qu.:0                             1st Qu.:  -2.3541                     
 Median :  -3.323                       Median :0                             Median :  -0.1743                     
 Mean   : -11.726                       Mean   :0                             Mean   :  -4.5402                     
 DiagnosticsVariables.Script.AccumDetach DiagnosticsVariables.Script.DeltaSoilOMN
 Min.   :0                               Min.   :-12180.53                       
 1st Qu.:0                               1st Qu.:  -200.92                       
 Median :0                               Median :   -91.00                       
 Mean   :0                               Mean   :  -327.08                       
 DiagnosticsVariables.Script.DeltaSurfaceOMN DiagnosticsVariables.Script.DeltaSoilMineralN Lucerne.Root.NSupply.Fixation
 Min.   :-16.0000                            Min.   :-258.73                               Min.   :0                    
 1st Qu.:  0.0000                            1st Qu.:-250.13                               1st Qu.:0                    
 Median :  0.0000                            Median :-170.57                               Median :0                    
 Mean   : -0.5151                            Mean   :-146.22                               Mean   :0                    
 Lucerne.Root.NSupply.Reallocation Lucerne.Root.NSupply.Retranslocation Lucerne.Root.NSupply.Uptake Soil.SoilWater.Eo 
 Min.   :0                         Min.   :0                            Min.   :0                   Min.   : 0.05379  
 1st Qu.:0                         1st Qu.:0                            1st Qu.:0                   1st Qu.: 1.54350  
 Median :0                         Median :0                            Median :0                   Median : 2.86545  
 Mean   :0                         Mean   :0                            Mean   :0                   Mean   : 3.24309  
 Soil.SoilWater.Es      SWC          DiagnosticsVariables.Script.DUL Soil.SoilWater.Drainage Soil.SoilWater.Runoff
 Min.   :0.0000    Min.   :  82.53   Min.   : 103.0                  Min.   : 0.0000         Min.   : 0.00000     
 1st Qu.:0.3311    1st Qu.: 480.80   1st Qu.: 723.0                  1st Qu.: 0.0000         1st Qu.: 0.00000     
 Median :0.5887    Median : 657.43   Median : 744.0                  Median : 0.0000         Median : 0.00000     
 Mean   :0.8110    Mean   : 661.72   Mean   : 815.2                  Mean   : 0.1053         Mean   : 0.01482     
 DiagnosticsVariables.Script.OutFlowLat DiagnosticsVariables.Script.AccumEO DiagnosticsVariables.Script.AccumEP
 Min.   :0                              Min.   :-9185.882                   Min.   :-2437.1                    
 1st Qu.:0                              1st Qu.:-2923.566                   1st Qu.: -971.0                    
 Median :0                              Median :-1652.845                   Median : -504.9                    
 Mean   :0                              Mean   :-2071.465                   Mean   : -604.6                    
 DiagnosticsVariables.Script.AccumES DiagnosticsVariables.Script.AccumDrainage DiagnosticsVariables.Script.AccumRunoff
 Min.   :-2096.227                   Min.   :-592.411                          Min.   :-217.719                       
 1st Qu.: -755.592                   1st Qu.: -57.094                          1st Qu.:  -8.244                       
 Median : -425.968                   Median :  -7.275                          Median :   0.000                       
 Mean   : -519.739                   Mean   : -65.213                          Mean   : -12.639                       
 DiagnosticsVariables.Script.AccumRainfall DiagnosticsVariables.Script.AccumIrrigation
 Min.   :   0.0                            Min.   :0                                  
 1st Qu.: 278.5                            1st Qu.:0                                  
 Median : 734.4                            Median :0                                  
 Mean   : 911.5                            Mean   :0                                  
 DiagnosticsVariables.Script.AccumOutflowLat DiagnosticsVariables.Script.SoilWaterDeficit Lucerne.Grain.Live.Wt
 Min.   :0                                   Min.   :-624.63                              Min.   :  0.000      
 1st Qu.:0                                   1st Qu.:-228.45                              1st Qu.:  0.000      
 Median :0                                   Median :-112.06                              Median :  0.000      
 Mean   :0                                   Mean   :-153.36                              Mean   :  2.965      
 Lucerne.Shell.Live.Wt     StemWt      Lucerne.Stem.Live.Wt Lucerne.Grain.Live.N Lucerne.Shell.Live.N     LeafWt      
 Min.   :  0.000       Min.   :    0   Min.   :   0.0       Min.   :0.00000      Min.   :0.00000      Min.   :   0.0  
 1st Qu.:  0.000       1st Qu.:  567   1st Qu.:  56.7       1st Qu.:0.00000      1st Qu.:0.00000      1st Qu.: 415.2  
 Median :  0.000       Median : 1467   Median : 146.7       Median :0.00000      Median :0.00000      Median : 854.4  
 Mean   :  2.965       Mean   : 2336   Mean   : 233.6       Mean   :0.08896      Mean   :0.08896      Mean   :1219.2  
     RootWt       Lucerne.Leaf.Live.N Lucerne.Root.Live.N Lucerne.Leaf.Live.NConc Lucerne.Root.Live.NConc
 Min.   :   0.0   Min.   :0.000e+00   Min.   :0.0000      Min.   :0.00e+00        Min.   :0.000000       
 1st Qu.: 149.4   1st Qu.:0.000e+00   1st Qu.:0.1472      1st Qu.:0.00e+00        1st Qu.:0.009827       
 Median : 275.3   Median :1.020e-08   Median :0.2709      Median :0.00e+00        Median :0.009935       
 Mean   : 412.0   Mean   :1.012e-04   Mean   :0.4081      Mean   :1.59e-05        Mean   :0.009691       
 Lucerne.Root.WaterUptake       ET          Lucerne.Root.Depth Lucerne.Leaf.CoverTotal Lucerne.Leaf.CoverDead
 Min.   :0.00000          Min.   : 0.0000   Min.   :   0       Min.   :0.00000         Min.   :0             
 1st Qu.:0.05364          1st Qu.: 0.6631   1st Qu.:1500       1st Qu.:0.04093         1st Qu.:0             
 Median :0.42055          Median : 1.3066   Median :2300       Median :0.55514         Median :0             
 Mean   :1.00188          Mean   : 1.8129   Mean   :1992       Mean   :0.55734         Mean   :0             
      LAI              Height           SWmm.1.         SWmm.2.          SWmm.3.          SWmm.4.           SWmm.5.       
 Min.   :0.00000   Min.   :   0.00   Min.   : 1.00   Min.   :  2.00   Min.   :  5.00   Min.   :  7.826   Min.   :  7.389  
 1st Qu.:0.05159   1st Qu.:  82.63   1st Qu.:20.07   1st Qu.: 22.11   1st Qu.: 23.12   1st Qu.: 23.590   1st Qu.: 25.299  
 Median :1.00000   Median : 222.92   Median :29.36   Median : 32.63   Median : 29.89   Median : 28.929   Median : 27.807  
 Mean   :2.29835   Mean   : 314.30   Mean   :29.57   Mean   : 32.36   Mean   : 36.09   Mean   : 41.060   Mean   : 43.080  
    SWmm.6.           SWmm.7.           SWmm.8.          SWmm.9.           SWmm.10.          SWmm.11.     
 Min.   :  7.291   Min.   :  7.227   Min.   :  7.33   Min.   :  7.222   Min.   :  7.313   Min.   :  7.59  
 1st Qu.: 25.611   1st Qu.: 24.288   1st Qu.: 22.89   1st Qu.: 22.218   1st Qu.: 20.757   1st Qu.: 19.73  
 Median : 29.683   Median : 31.479   Median : 30.82   Median : 31.464   Median : 31.341   Median : 31.47  
 Mean   : 44.685   Mean   : 46.412   Mean   : 45.17   Mean   : 44.535   Mean   : 44.817   Mean   : 39.12  
    SWmm.12.         SWmm.13.        SWmm.14.        SWmm.15.        SWmm.16.        SWmm.17.        SWmm.18.    
 Min.   :  8.58   Min.   : 8.68   Min.   : 9.10   Min.   :10.00   Min.   :10.23   Min.   : 9.83   Min.   : 9.78  
 1st Qu.: 20.67   1st Qu.:20.06   1st Qu.:19.55   1st Qu.:20.23   1st Qu.:21.79   1st Qu.:25.25   1st Qu.:25.24  
 Median : 29.86   Median :28.00   Median :29.81   Median :30.92   Median :30.76   Median :29.39   Median :29.97  
 Mean   : 31.15   Mean   :29.64   Mean   :29.97   Mean   :30.64   Mean   :31.60   Mean   :29.77   Mean   :30.01  
    SWmm.19.        SWmm.20.        SWmm.21.        SWmm.22.        SWmm.23.      Soil.NO3N.1.      Soil.NO3N.2.    
 Min.   : 9.72   Min.   : 9.99   Min.   :10.18   Min.   :10.69   Min.   :15.03   Min.   :  0.000   Min.   : 0.1319  
 1st Qu.:25.26   1st Qu.:25.34   1st Qu.:23.55   1st Qu.:18.39   1st Qu.:21.84   1st Qu.:  1.431   1st Qu.: 1.5505  
 Median :29.94   Median :30.55   Median :30.63   Median :30.80   Median :33.15   Median :  2.600   Median : 2.4889  
 Mean   :30.22   Mean   :30.52   Mean   :30.61   Mean   :29.32   Mean   :31.69   Mean   : 13.187   Mean   : 6.3640  
  Soil.NO3N.3.      Soil.NO3N.4.      Soil.NO3N.5.      Soil.NO3N.6.     Soil.NO3N.7.     Soil.NO3N.8.     Soil.NO3N.9.   
 Min.   : 0.2321   Min.   : 0.2857   Min.   : 0.3562   Min.   : 0.486   Min.   : 0.426   Min.   : 0.432   Min.   : 0.281  
 1st Qu.: 1.5254   1st Qu.: 1.2642   1st Qu.: 1.2458   1st Qu.: 1.279   1st Qu.: 1.042   1st Qu.: 1.007   1st Qu.: 0.934  
 Median : 2.4029   Median : 1.9555   Median : 1.7426   Median : 1.760   Median : 1.499   Median : 1.364   Median : 1.223  
 Mean   : 5.8377   Mean   : 4.5441   Mean   : 3.8181   Mean   : 3.104   Mean   : 2.523   Mean   : 1.821   Mean   : 1.675  
 Soil.NO3N.10.    Soil.NO3N.11.    Soil.NO3N.12.    Soil.NO3N.13.    Soil.NO3N.14.    Soil.NO3N.15.    Soil.NO3N.16.   
 Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.058  
 1st Qu.: 0.609   1st Qu.: 0.468   1st Qu.: 0.334   1st Qu.: 0.255   1st Qu.: 0.224   1st Qu.: 0.233   1st Qu.: 0.442  
 Median : 0.837   Median : 0.708   Median : 0.584   Median : 0.531   Median : 0.506   Median : 0.473   Median : 0.515  
 Mean   : 1.316   Mean   : 1.388   Mean   : 1.161   Mean   : 1.136   Mean   : 1.101   Mean   : 1.073   Mean   : 1.220  
 Soil.NO3N.17.   Soil.NO3N.18.   Soil.NO3N.19.   Soil.NO3N.20.   Soil.NO3N.21.   Soil.NO3N.22.   Soil.NO3N.23.  
 Min.   :0.149   Min.   :0.098   Min.   :0.074   Min.   :0.095   Min.   :0.098   Min.   :0.097   Min.   :0.140  
 1st Qu.:0.373   1st Qu.:0.345   1st Qu.:0.320   1st Qu.:0.310   1st Qu.:0.315   1st Qu.:0.335   1st Qu.:0.379  
 Median :0.468   Median :0.435   Median :0.417   Median :0.405   Median :0.412   Median :0.464   Median :0.478  
 Mean   :0.779   Mean   :0.738   Mean   :0.739   Mean   :0.751   Mean   :0.796   Mean   :0.821   Mean   :0.806  
 DiagnosticsVariables.Script.SoilNitrogenContent Lucerne.Arbitrator.N.TotalPlantDemand DiagnosticsVariables.Script.FomN
 Min.   :  0.00                                  Min.   :0.00000                       Min.   : 0.0000                 
 1st Qu.: 20.47                                  1st Qu.:0.01241                       1st Qu.: 0.3537                 
 Median : 26.75                                  Median :0.08534                       Median : 1.0975                 
 Mean   : 54.01                                  Mean   :0.18648                       Mean   : 3.3132                 
 DiagnosticsVariables.Script.HumN DiagnosticsVariables.Script.BiomN DiagnosticsVariables.Script.DltNMinRes
 Min.   :    0                    Min.   :  0.00                    Min.   :-1.679280                     
 1st Qu.:11015                    1st Qu.: 87.43                    1st Qu.: 0.000000                     
 Median :22728                    Median :128.38                    Median : 0.000000                     
 Mean   :17206                    Mean   :126.37                    Mean   :-0.001052                     
 DiagnosticsVariables.Script.DltNMinTot Lucerne.Leaf.Fw  Lucerne.Leaf.Fn     Lucerne.Phenology.CurrentPhaseName
 Min.   :-0.07311                       Min.   :0.0000   Min.   :0.0000000   Length:56703                      
 1st Qu.: 0.07441                       1st Qu.:1.0000   1st Qu.:0.0000000   Class :character                  
 Median : 0.15414                       Median :1.0000   Median :0.0000001   Mode  :character                  
 Mean   : 0.19326                       Mean   :0.8591   Mean   :0.0532373                                     
 Lucerne.Phenology.CurrentStageName Lucerne.Phenology.Stage Lucerne.Pod.Wt    Lucerne.Pod.N      shootbiomass  
 Length:56703                       Min.   :1.000           Min.   :  0.000   Min.   : 0.0000   Min.   :    0  
 Class :character                   1st Qu.:4.310           1st Qu.:  0.000   1st Qu.: 0.0000   1st Qu.: 1008  
 Mode  :character                   Median :4.808           Median :  0.000   Median : 0.0000   Median : 2316  
                                    Mean   :4.939           Mean   :  5.931   Mean   : 0.1779   Mean   : 3614  
 Lucerne.Root.LengthDensity.1. Lucerne.Root.LengthDensity.2. Lucerne.Root.LengthDensity.3. Lucerne.Root.LengthDensity.4.
 Min.   :0.000000              Min.   :0.0000000             Min.   :0.0000000             Min.   :0.0000000            
 1st Qu.:0.000561              1st Qu.:0.0004222             1st Qu.:0.0002453             1st Qu.:0.0001412            
 Median :0.001456              Median :0.0010220             Median :0.0006460             Median :0.0004311            
 Mean   :0.003661              Mean   :0.0016247             Mean   :0.0010904             Mean   :0.0007684            
 Lucerne.Root.LengthDensity.5. Lucerne.Root.LengthDensity.6. Lucerne.Root.LengthDensity.7. Lucerne.Root.LengthDensity.8.
 Min.   :0.000e+00             Min.   :0.0000                Min.   :0.0000                Min.   :0.000                
 1st Qu.:5.606e-05             1st Qu.:0.0000                1st Qu.:0.0000                1st Qu.:0.000                
 Median :3.537e-04             Median :0.0003                Median :0.0003                Median :0.000                
 Mean   :6.366e-04             Mean   :0.0006                Mean   :0.0006                Mean   :0.001                
 Lucerne.Root.LengthDensity.9. Lucerne.Root.LengthDensity.10. Lucerne.Root.LengthDensity.11.
 Min.   :0.000                 Min.   :0.000                  Min.   :0.000                 
 1st Qu.:0.000                 1st Qu.:0.000                  1st Qu.:0.000                 
 Median :0.000                 Median :0.000                  Median :0.000                 
 Mean   :0.001                 Mean   :0.000                  Mean   :0.000                 
 Lucerne.Root.LengthDensity.12. Lucerne.Root.LengthDensity.13. Lucerne.Root.LengthDensity.14.
 Min.   :0.000                  Min.   :0.000                  Min.   :0.000                 
 1st Qu.:0.000                  1st Qu.:0.000                  1st Qu.:0.000                 
 Median :0.000                  Median :0.000                  Median :0.000                 
 Mean   :0.000                  Mean   :0.000                  Mean   :0.000                 
 Lucerne.Root.LengthDensity.15. Lucerne.Root.LengthDensity.16. Lucerne.Root.LengthDensity.17.
 Min.   :0.000                  Min.   :0.000                  Min.   :0.000                 
 1st Qu.:0.000                  1st Qu.:0.000                  1st Qu.:0.000                 
 Median :0.000                  Median :0.000                  Median :0.000                 
 Mean   :0.000                  Mean   :0.000                  Mean   :0.000                 
 Lucerne.Root.LengthDensity.18. Lucerne.Root.LengthDensity.19. Lucerne.Root.LengthDensity.20.
 Min.   :0.000                  Min.   :0.000                  Min.   :0.000                 
 1st Qu.:0.000                  1st Qu.:0.000                  1st Qu.:0.000                 
 Median :0.000                  Median :0.000                  Median :0.000                 
 Mean   :0.000                  Mean   :0.000                  Mean   :0.000                 
 Lucerne.Root.LengthDensity.21. Lucerne.Root.LengthDensity.22. Lucerne.Root.LengthDensity.23. Soil.SoilWater.WaterTable
 Min.   :0.000                  Min.   :0.000                  Min.   :0.000                  Min.   :   0.763         
 1st Qu.:0.000                  1st Qu.:0.000                  1st Qu.:0.000                  1st Qu.:1500.000         
 Median :0.000                  Median :0.000                  Median :0.000                  Median :2300.000         
 Mean   :0.000                  Mean   :0.000                  Mean   :0.000                  Mean   :2043.145         
 Lucerne.AboveGround.Wt Lucerne.AboveGround.N Soil.SoilWater.ESW.1. Soil.SoilWater.ESW.2. Soil.SoilWater.ESW.3.
 Min.   :   0.0         Min.   : 0.000        Min.   : 0.00         Min.   : 0.000        Min.   : 0.00        
 1st Qu.: 100.8         1st Qu.: 1.709        1st Qu.: 6.85         1st Qu.: 8.134        1st Qu.: 6.00        
 Median : 231.6         Median : 4.422        Median :16.71         Median :19.774        Median :17.85        
 Mean   : 361.4         Mean   : 7.140        Mean   :15.63         Mean   :17.125        Mean   :16.00        
 Soil.SoilWater.ESW.4. Soil.SoilWater.ESW.5. Soil.SoilWater.ESW.6. Soil.SoilWater.ESW.7. Soil.SoilWater.ESW.8.
 Min.   : 0.000        Min.   :  0.000       Min.   : 0.000        Min.   : 0.000        Min.   : 0.00        
 1st Qu.: 3.823        1st Qu.:  2.194       1st Qu.: 1.203        1st Qu.: 2.039        1st Qu.: 3.33        
 Median :16.289        Median : 14.646       Median :15.028        Median :14.378        Median :14.72        
 Mean   :14.744        Mean   : 13.601       Mean   :14.014        Mean   :13.349        Mean   :14.69        
 Soil.SoilWater.ESW.9. Soil.SoilWater.ESW.10. Soil.SoilWater.ESW.11. Soil.SoilWater.ESW.12. Soil.SoilWater.ESW.13.
 Min.   : 0.000        Min.   : 0.000         Min.   : 0.000         Min.   : 0.000         Min.   : 0.000        
 1st Qu.: 2.195        1st Qu.: 1.617         1st Qu.: 2.878         1st Qu.: 8.027         1st Qu.: 7.445        
 Median :13.891        Median :13.651         Median :15.098         Median :16.565         Median :15.733        
 Mean   :13.528        Mean   :13.655         Mean   :14.100         Mean   :15.615         Mean   :14.553        
 Soil.SoilWater.ESW.14. Soil.SoilWater.ESW.15. Soil.SoilWater.ESW.16. Soil.SoilWater.ESW.17. Soil.SoilWater.ESW.18.
 Min.   : 0.000         Min.   : 0.000         Min.   : 0.00          Min.   : 0.00          Min.   : 0.00         
 1st Qu.: 7.172         1st Qu.: 8.141         1st Qu.:11.02          1st Qu.:14.00          1st Qu.:14.00         
 Median :16.238         Median :16.571         Median :16.94          Median :18.69          Median :18.63         
 Mean   :14.820         Mean   :15.538         Mean   :15.72          Mean   :16.90          Mean   :17.14         
 Soil.SoilWater.ESW.19. Soil.SoilWater.ESW.20. Soil.SoilWater.ESW.21. Soil.SoilWater.ESW.22. Soil.SoilWater.ESW.23.
 Min.   : 0.00          Min.   : 0.00          Min.   : 0.00          Min.   : 0.00          Min.   : 6.025        
 1st Qu.:14.00          1st Qu.:15.72          1st Qu.:14.02          1st Qu.: 8.32          1st Qu.:12.608        
 Median :18.90          Median :20.30          Median :18.98          Median :18.16          Median :21.281        
 Mean   :17.36          Mean   :18.30          Mean   :17.57          Mean   :16.26          Mean   :19.715        
  CheckpointID  SowingDate        Defoliation             FD              Factors          Soil.OutputLayers.SWmm.1.
 Min.   :1     Length:56703       Length:56703       Length:56703       Length:56703       Min.   :  3.00           
 1st Qu.:1     Class :character   Class :character   Class :character   Class :character   1st Qu.: 14.34           
 Median :1     Mode  :character   Mode  :character   Mode  :character   Mode  :character   Median : 21.67           
 Mean   :1                                                                                 Mean   : 25.46           
 Soil.OutputLayers.SWmm.2. Soil.OutputLayers.SWmm.3. Soil.OutputLayers.SWmm.4. Soil.OutputLayers.SWmm.5.
 Min.   : 21.00            Min.   : 42.09            Min.   :  0.00            Min.   : 51.35           
 1st Qu.: 29.67            1st Qu.: 55.27            1st Qu.: 52.00            1st Qu.: 86.27           
 Median : 63.91            Median : 71.42            Median : 80.01            Median :119.08           
 Mean   : 76.81            Mean   : 87.61            Mean   : 87.10            Mean   :117.11           
 Soil.OutputLayers.SWmm.6. Soil.OutputLayers.SWmm.7. Soil.OutputLayers.SWmm.8. Soil.OutputLayers.SW.1.
 Min.   : 54.79            Min.   : 57.97            Min.   : 60.89            Min.   :0.03           
 1st Qu.: 74.11            1st Qu.: 90.00            1st Qu.: 88.00            1st Qu.:0.07           
 Median : 93.97            Median : 99.01            Median : 92.08            Median :0.18           
 Mean   :105.21            Mean   :108.70            Mean   :137.56            Mean   :0.19           
 Soil.OutputLayers.SW.2. Soil.OutputLayers.SW.3. Soil.OutputLayers.SW.4. Soil.OutputLayers.SW.5. Soil.OutputLayers.SW.6.
 Min.   :0.05            Min.   :0.05            Min.   :0.00            Min.   :0.22            Min.   :0.19           
 1st Qu.:0.10            1st Qu.:0.16            1st Qu.:0.20            1st Qu.:0.24            1st Qu.:0.21           
 Median :0.23            Median :0.24            Median :0.25            Median :0.29            Median :0.28           
 Mean   :0.23            Mean   :0.24            Mean   :0.25            Mean   :0.30            Mean   :0.29           
 Soil.OutputLayers.SW.7. Soil.OutputLayers.SW.8. Soil.OutputLayers.SWmm.9. Soil.OutputLayers.SWmm.10.
 Min.   :0.22            Min.   :0.22            Min.   :63.01             Min.   :64.02             
 1st Qu.:0.23            1st Qu.:0.22            1st Qu.:63.35             1st Qu.:64.34             
 Median :0.29            Median :0.31            Median :63.71             Median :64.77             
 Mean   :0.30            Mean   :0.30            Mean   :63.67             Mean   :64.87             
 Soil.OutputLayers.SWmm.11. Soil.OutputLayers.SWmm.12. Soil.OutputLayers.SWmm.13. Soil.OutputLayers.SWmm.14.
 Min.   :64.00              Min.   :60.11              Min.   :58.00              Min.   :54.00             
 1st Qu.:64.00              1st Qu.:60.19              1st Qu.:58.00              1st Qu.:54.02             
 Median :64.00              Median :60.36              Median :58.00              Median :54.20             
 Mean   :64.36              Mean   :60.74              Mean   :58.00              Mean   :54.24             
 Soil.OutputLayers.SWmm.15. Soil.OutputLayers.SWmm.16. Soil.OutputLayers.SWmm.17. Soil.OutputLayers.SWmm.18.
 Min.   :54.00              Min.   :54.00              Min.   :54.53              Min.   :58.01             
 1st Qu.:54.02              1st Qu.:54.38              1st Qu.:54.68              1st Qu.:58.85             
 Median :54.08              Median :54.47              Median :54.82              Median :59.14             
 Mean   :54.06              Mean   :54.42              Mean   :55.14              Mean   :59.06             
 Soil.OutputLayers.SWmm.19. Soil.OutputLayers.SWmm.20. Soil.OutputLayers.SWmm.21. Soil.OutputLayers.SWmm.22.
 Min.   :60.91              Min.   :61.30              Min.   :59.00              Min.   :51.25             
 1st Qu.:61.73              1st Qu.:62.57              1st Qu.:60.73              1st Qu.:51.98             
 Median :62.52              Median :63.87              Median :63.27              Median :53.73             
 Mean   :62.49              Mean   :64.01              Mean   :63.77              Mean   :56.44             
 Soil.OutputLayers.SWmm.23. Soil.OutputLayers.SW.9. Soil.OutputLayers.SW.10. Soil.OutputLayers.SW.11.
 Min.   :0                  Min.   :0.32            Min.   :0.32             Min.   :0.32            
 1st Qu.:0                  1st Qu.:0.32            1st Qu.:0.32             1st Qu.:0.32            
 Median :0                  Median :0.32            Median :0.32             Median :0.32            
 Mean   :0                  Mean   :0.32            Mean   :0.32             Mean   :0.32            
 Soil.OutputLayers.SW.12. Soil.OutputLayers.SW.13. Soil.OutputLayers.SW.14. Soil.OutputLayers.SW.15.
 Min.   :0.30             Min.   :0.29             Min.   :0.27             Min.   :0.27            
 1st Qu.:0.30             1st Qu.:0.29             1st Qu.:0.27             1st Qu.:0.27            
 Median :0.30             Median :0.29             Median :0.27             Median :0.27            
 Mean   :0.30             Mean   :0.29             Mean   :0.27             Mean   :0.27            
 Soil.OutputLayers.SW.16. Soil.OutputLayers.SW.17. Soil.OutputLayers.SW.18. Soil.OutputLayers.SW.19.
 Min.   :0.27             Min.   :0.27             Min.   :0.29             Min.   :0.30            
 1st Qu.:0.27             1st Qu.:0.27             1st Qu.:0.29             1st Qu.:0.31            
 Median :0.27             Median :0.27             Median :0.30             Median :0.31            
 Mean   :0.27             Mean   :0.28             Mean   :0.30             Mean   :0.31            
 Soil.OutputLayers.SW.20. Soil.OutputLayers.SW.21. Soil.OutputLayers.SW.22. Soil.OutputLayers.SW.23.    SWmm.24.    
 Min.   :0.31             Min.   :0.30             Min.   :0.26             Min.   :0                Min.   :58.00  
 1st Qu.:0.31             1st Qu.:0.30             1st Qu.:0.26             1st Qu.:0                1st Qu.:58.78  
 Median :0.32             Median :0.32             Median :0.27             Median :0                Median :60.00  
 Mean   :0.32             Mean   :0.32             Mean   :0.28             Mean   :0                Mean   :59.43  
    SWmm.25.        SWmm.26.        SWmm.27.     Soil.NO3N.24.   Soil.NO3N.25.   Soil.NO3N.26.   Soil.NO3N.27.  
 Min.   :58.00   Min.   :58.00   Min.   :58.00   Min.   :0.04    Min.   :0.00    Min.   :0.00    Min.   :0.00   
 1st Qu.:60.00   1st Qu.:60.00   1st Qu.:60.00   1st Qu.:0.21    1st Qu.:0.02    1st Qu.:0.02    1st Qu.:0.02   
 Median :60.00   Median :60.00   Median :60.00   Median :0.28    Median :0.19    Median :0.19    Median :0.19   
 Mean   :59.76   Mean   :59.75   Mean   :59.75   Mean   :1.10    Mean   :1.03    Mean   :1.03    Mean   :1.04   
 Lucerne.Root.LengthDensity.24. Lucerne.Root.LengthDensity.25. Lucerne.Root.LengthDensity.26.
 Min.   :0                      Min.   :0                      Min.   :0                     
 1st Qu.:0                      1st Qu.:0                      1st Qu.:0                     
 Median :0                      Median :0                      Median :0                     
 Mean   :0                      Mean   :0                      Mean   :0                     
 Lucerne.Root.LengthDensity.27. Soil.SoilWater.ESW.24. Soil.SoilWater.ESW.25. Soil.SoilWater.ESW.26.
 Min.   :0                      Min.   :14.00          Min.   :14.00          Min.   :14.00         
 1st Qu.:0                      1st Qu.:14.78          1st Qu.:16.00          1st Qu.:16.00         
 Median :0                      Median :16.00          Median :16.00          Median :16.00         
 Mean   :0                      Mean   :15.43          Mean   :15.76          Mean   :15.75         
 Soil.SoilWater.ESW.27. Lucerne.Phenology.DaysAfterCutting.Value.. Lucerne.Phenology.FloweringDaysAfterCutting.Value..
 Min.   :14.00          Min.   :  0.00                             Min.   :  0.00                                     
 1st Qu.:16.00          1st Qu.: 13.00                             1st Qu.:  0.00                                     
 Median :16.00          Median : 30.00                             Median :  0.00                                     
 Mean   :15.75          Mean   : 33.22                             Mean   : 23.65                                     
   NodeNumber     Lucerne.Leaf.HeightFunction.DeltaHeight.Value.. Lucerne.Leaf.LAIFunction.Value..
 Min.   : 0.000   Min.   : 0.000                                  Min.   :0.05000                 
 1st Qu.: 2.688   1st Qu.: 2.020                                  1st Qu.:0.05159                 
 Median : 6.317   Median : 5.416                                  Median :1.00000                 
 Mean   : 8.911   Mean   : 8.122                                  Mean   :2.30092                 
 [ reached getOption("max.print") -- omitted 3 rows ]
head(df) # simulation results

get sim names (different table)

merge names

remove unecessary variables

simNameDf <- as.data.frame (GetApsimNGTable(db.address,"_Simulations"))
myDb <- merge(df, simNameDf, by.x= c("SimulationID"), by.y= c("ID"))
#str(myDb)
head(myDb)
summary(myDb)
  SimulationID      Water               Zone            Clock.Today                 
 Min.   : 1.00   Length:56703       Length:56703       Min.   :1979-01-01 12:00:00  
 1st Qu.:14.00   Class :character   Class :character   1st Qu.:1998-11-20 12:00:00  
 Median :40.00   Mode  :character   Mode  :character   Median :2001-11-30 12:00:00  
 Mean   :34.87                                         Mean   :2004-04-22 12:38:26  
 DiagnosticsVariables.Script.AccumPlantN DiagnosticsVariables.Script.AccumMineralisation
 Min.   :  0.0                           Min.   : -0.1345                               
 1st Qu.:113.7                           1st Qu.: 48.0568                               
 Median :278.8                           Median : 97.2750                               
 Mean   :253.9                           Mean   :132.5251                               
 DiagnosticsVariables.Script.AccumDenit DiagnosticsVariables.Script.AccumFert DiagnosticsVariables.Script.AccumLeach
 Min.   :-120.284                       Min.   :0                             Min.   :-127.0290                     
 1st Qu.: -11.333                       1st Qu.:0                             1st Qu.:  -2.3541                     
 Median :  -3.323                       Median :0                             Median :  -0.1743                     
 Mean   : -11.726                       Mean   :0                             Mean   :  -4.5402                     
 DiagnosticsVariables.Script.AccumDetach DiagnosticsVariables.Script.DeltaSoilOMN
 Min.   :0                               Min.   :-12180.53                       
 1st Qu.:0                               1st Qu.:  -200.92                       
 Median :0                               Median :   -91.00                       
 Mean   :0                               Mean   :  -327.08                       
 DiagnosticsVariables.Script.DeltaSurfaceOMN DiagnosticsVariables.Script.DeltaSoilMineralN Lucerne.Root.NSupply.Fixation
 Min.   :-16.0000                            Min.   :-258.73                               Min.   :0                    
 1st Qu.:  0.0000                            1st Qu.:-250.13                               1st Qu.:0                    
 Median :  0.0000                            Median :-170.57                               Median :0                    
 Mean   : -0.5151                            Mean   :-146.22                               Mean   :0                    
 Lucerne.Root.NSupply.Reallocation Lucerne.Root.NSupply.Retranslocation Lucerne.Root.NSupply.Uptake Soil.SoilWater.Eo 
 Min.   :0                         Min.   :0                            Min.   :0                   Min.   : 0.05379  
 1st Qu.:0                         1st Qu.:0                            1st Qu.:0                   1st Qu.: 1.54350  
 Median :0                         Median :0                            Median :0                   Median : 2.86545  
 Mean   :0                         Mean   :0                            Mean   :0                   Mean   : 3.24309  
 Soil.SoilWater.Es      SWC          DiagnosticsVariables.Script.DUL Soil.SoilWater.Drainage Soil.SoilWater.Runoff
 Min.   :0.0000    Min.   :  82.53   Min.   : 103.0                  Min.   : 0.0000         Min.   : 0.00000     
 1st Qu.:0.3311    1st Qu.: 480.80   1st Qu.: 723.0                  1st Qu.: 0.0000         1st Qu.: 0.00000     
 Median :0.5887    Median : 657.43   Median : 744.0                  Median : 0.0000         Median : 0.00000     
 Mean   :0.8110    Mean   : 661.72   Mean   : 815.2                  Mean   : 0.1053         Mean   : 0.01482     
 DiagnosticsVariables.Script.OutFlowLat DiagnosticsVariables.Script.AccumEO DiagnosticsVariables.Script.AccumEP
 Min.   :0                              Min.   :-9185.882                   Min.   :-2437.1                    
 1st Qu.:0                              1st Qu.:-2923.566                   1st Qu.: -971.0                    
 Median :0                              Median :-1652.845                   Median : -504.9                    
 Mean   :0                              Mean   :-2071.465                   Mean   : -604.6                    
 DiagnosticsVariables.Script.AccumES DiagnosticsVariables.Script.AccumDrainage DiagnosticsVariables.Script.AccumRunoff
 Min.   :-2096.227                   Min.   :-592.411                          Min.   :-217.719                       
 1st Qu.: -755.592                   1st Qu.: -57.094                          1st Qu.:  -8.244                       
 Median : -425.968                   Median :  -7.275                          Median :   0.000                       
 Mean   : -519.739                   Mean   : -65.213                          Mean   : -12.639                       
 DiagnosticsVariables.Script.AccumRainfall DiagnosticsVariables.Script.AccumIrrigation
 Min.   :   0.0                            Min.   :0                                  
 1st Qu.: 278.5                            1st Qu.:0                                  
 Median : 734.4                            Median :0                                  
 Mean   : 911.5                            Mean   :0                                  
 DiagnosticsVariables.Script.AccumOutflowLat DiagnosticsVariables.Script.SoilWaterDeficit Lucerne.Grain.Live.Wt
 Min.   :0                                   Min.   :-624.63                              Min.   :  0.000      
 1st Qu.:0                                   1st Qu.:-228.45                              1st Qu.:  0.000      
 Median :0                                   Median :-112.06                              Median :  0.000      
 Mean   :0                                   Mean   :-153.36                              Mean   :  2.965      
 Lucerne.Shell.Live.Wt     StemWt      Lucerne.Stem.Live.Wt Lucerne.Grain.Live.N Lucerne.Shell.Live.N     LeafWt      
 Min.   :  0.000       Min.   :    0   Min.   :   0.0       Min.   :0.00000      Min.   :0.00000      Min.   :   0.0  
 1st Qu.:  0.000       1st Qu.:  567   1st Qu.:  56.7       1st Qu.:0.00000      1st Qu.:0.00000      1st Qu.: 415.2  
 Median :  0.000       Median : 1467   Median : 146.7       Median :0.00000      Median :0.00000      Median : 854.4  
 Mean   :  2.965       Mean   : 2336   Mean   : 233.6       Mean   :0.08896      Mean   :0.08896      Mean   :1219.2  
     RootWt       Lucerne.Leaf.Live.N Lucerne.Root.Live.N Lucerne.Leaf.Live.NConc Lucerne.Root.Live.NConc
 Min.   :   0.0   Min.   :0.000e+00   Min.   :0.0000      Min.   :0.00e+00        Min.   :0.000000       
 1st Qu.: 149.4   1st Qu.:0.000e+00   1st Qu.:0.1472      1st Qu.:0.00e+00        1st Qu.:0.009827       
 Median : 275.3   Median :1.020e-08   Median :0.2709      Median :0.00e+00        Median :0.009935       
 Mean   : 412.0   Mean   :1.012e-04   Mean   :0.4081      Mean   :1.59e-05        Mean   :0.009691       
 Lucerne.Root.WaterUptake       ET          Lucerne.Root.Depth Lucerne.Leaf.CoverTotal Lucerne.Leaf.CoverDead
 Min.   :0.00000          Min.   : 0.0000   Min.   :   0       Min.   :0.00000         Min.   :0             
 1st Qu.:0.05364          1st Qu.: 0.6631   1st Qu.:1500       1st Qu.:0.04093         1st Qu.:0             
 Median :0.42055          Median : 1.3066   Median :2300       Median :0.55514         Median :0             
 Mean   :1.00188          Mean   : 1.8129   Mean   :1992       Mean   :0.55734         Mean   :0             
      LAI              Height           SWmm.1.         SWmm.2.          SWmm.3.          SWmm.4.           SWmm.5.       
 Min.   :0.00000   Min.   :   0.00   Min.   : 1.00   Min.   :  2.00   Min.   :  5.00   Min.   :  7.826   Min.   :  7.389  
 1st Qu.:0.05159   1st Qu.:  82.63   1st Qu.:20.07   1st Qu.: 22.11   1st Qu.: 23.12   1st Qu.: 23.590   1st Qu.: 25.299  
 Median :1.00000   Median : 222.92   Median :29.36   Median : 32.63   Median : 29.89   Median : 28.929   Median : 27.807  
 Mean   :2.29835   Mean   : 314.30   Mean   :29.57   Mean   : 32.36   Mean   : 36.09   Mean   : 41.060   Mean   : 43.080  
    SWmm.6.           SWmm.7.           SWmm.8.          SWmm.9.           SWmm.10.          SWmm.11.     
 Min.   :  7.291   Min.   :  7.227   Min.   :  7.33   Min.   :  7.222   Min.   :  7.313   Min.   :  7.59  
 1st Qu.: 25.611   1st Qu.: 24.288   1st Qu.: 22.89   1st Qu.: 22.218   1st Qu.: 20.757   1st Qu.: 19.73  
 Median : 29.683   Median : 31.479   Median : 30.82   Median : 31.464   Median : 31.341   Median : 31.47  
 Mean   : 44.685   Mean   : 46.412   Mean   : 45.17   Mean   : 44.535   Mean   : 44.817   Mean   : 39.12  
    SWmm.12.         SWmm.13.        SWmm.14.        SWmm.15.        SWmm.16.        SWmm.17.        SWmm.18.    
 Min.   :  8.58   Min.   : 8.68   Min.   : 9.10   Min.   :10.00   Min.   :10.23   Min.   : 9.83   Min.   : 9.78  
 1st Qu.: 20.67   1st Qu.:20.06   1st Qu.:19.55   1st Qu.:20.23   1st Qu.:21.79   1st Qu.:25.25   1st Qu.:25.24  
 Median : 29.86   Median :28.00   Median :29.81   Median :30.92   Median :30.76   Median :29.39   Median :29.97  
 Mean   : 31.15   Mean   :29.64   Mean   :29.97   Mean   :30.64   Mean   :31.60   Mean   :29.77   Mean   :30.01  
    SWmm.19.        SWmm.20.        SWmm.21.        SWmm.22.        SWmm.23.      Soil.NO3N.1.      Soil.NO3N.2.    
 Min.   : 9.72   Min.   : 9.99   Min.   :10.18   Min.   :10.69   Min.   :15.03   Min.   :  0.000   Min.   : 0.1319  
 1st Qu.:25.26   1st Qu.:25.34   1st Qu.:23.55   1st Qu.:18.39   1st Qu.:21.84   1st Qu.:  1.431   1st Qu.: 1.5505  
 Median :29.94   Median :30.55   Median :30.63   Median :30.80   Median :33.15   Median :  2.600   Median : 2.4889  
 Mean   :30.22   Mean   :30.52   Mean   :30.61   Mean   :29.32   Mean   :31.69   Mean   : 13.187   Mean   : 6.3640  
  Soil.NO3N.3.      Soil.NO3N.4.      Soil.NO3N.5.      Soil.NO3N.6.     Soil.NO3N.7.     Soil.NO3N.8.     Soil.NO3N.9.   
 Min.   : 0.2321   Min.   : 0.2857   Min.   : 0.3562   Min.   : 0.486   Min.   : 0.426   Min.   : 0.432   Min.   : 0.281  
 1st Qu.: 1.5254   1st Qu.: 1.2642   1st Qu.: 1.2458   1st Qu.: 1.279   1st Qu.: 1.042   1st Qu.: 1.007   1st Qu.: 0.934  
 Median : 2.4029   Median : 1.9555   Median : 1.7426   Median : 1.760   Median : 1.499   Median : 1.364   Median : 1.223  
 Mean   : 5.8377   Mean   : 4.5441   Mean   : 3.8181   Mean   : 3.104   Mean   : 2.523   Mean   : 1.821   Mean   : 1.675  
 Soil.NO3N.10.    Soil.NO3N.11.    Soil.NO3N.12.    Soil.NO3N.13.    Soil.NO3N.14.    Soil.NO3N.15.    Soil.NO3N.16.   
 Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.058  
 1st Qu.: 0.609   1st Qu.: 0.468   1st Qu.: 0.334   1st Qu.: 0.255   1st Qu.: 0.224   1st Qu.: 0.233   1st Qu.: 0.442  
 Median : 0.837   Median : 0.708   Median : 0.584   Median : 0.531   Median : 0.506   Median : 0.473   Median : 0.515  
 Mean   : 1.316   Mean   : 1.388   Mean   : 1.161   Mean   : 1.136   Mean   : 1.101   Mean   : 1.073   Mean   : 1.220  
 Soil.NO3N.17.   Soil.NO3N.18.   Soil.NO3N.19.   Soil.NO3N.20.   Soil.NO3N.21.   Soil.NO3N.22.   Soil.NO3N.23.  
 Min.   :0.149   Min.   :0.098   Min.   :0.074   Min.   :0.095   Min.   :0.098   Min.   :0.097   Min.   :0.140  
 1st Qu.:0.373   1st Qu.:0.345   1st Qu.:0.320   1st Qu.:0.310   1st Qu.:0.315   1st Qu.:0.335   1st Qu.:0.379  
 Median :0.468   Median :0.435   Median :0.417   Median :0.405   Median :0.412   Median :0.464   Median :0.478  
 Mean   :0.779   Mean   :0.738   Mean   :0.739   Mean   :0.751   Mean   :0.796   Mean   :0.821   Mean   :0.806  
 DiagnosticsVariables.Script.SoilNitrogenContent Lucerne.Arbitrator.N.TotalPlantDemand DiagnosticsVariables.Script.FomN
 Min.   :  0.00                                  Min.   :0.00000                       Min.   : 0.0000                 
 1st Qu.: 20.47                                  1st Qu.:0.01241                       1st Qu.: 0.3537                 
 Median : 26.75                                  Median :0.08534                       Median : 1.0975                 
 Mean   : 54.01                                  Mean   :0.18648                       Mean   : 3.3132                 
 DiagnosticsVariables.Script.HumN DiagnosticsVariables.Script.BiomN DiagnosticsVariables.Script.DltNMinRes
 Min.   :    0                    Min.   :  0.00                    Min.   :-1.679280                     
 1st Qu.:11015                    1st Qu.: 87.43                    1st Qu.: 0.000000                     
 Median :22728                    Median :128.38                    Median : 0.000000                     
 Mean   :17206                    Mean   :126.37                    Mean   :-0.001052                     
 DiagnosticsVariables.Script.DltNMinTot Lucerne.Leaf.Fw  Lucerne.Leaf.Fn     Lucerne.Phenology.CurrentPhaseName
 Min.   :-0.07311                       Min.   :0.0000   Min.   :0.0000000   Length:56703                      
 1st Qu.: 0.07441                       1st Qu.:1.0000   1st Qu.:0.0000000   Class :character                  
 Median : 0.15414                       Median :1.0000   Median :0.0000001   Mode  :character                  
 Mean   : 0.19326                       Mean   :0.8591   Mean   :0.0532373                                     
 Lucerne.Phenology.CurrentStageName Lucerne.Phenology.Stage Lucerne.Pod.Wt    Lucerne.Pod.N      shootbiomass  
 Length:56703                       Min.   :1.000           Min.   :  0.000   Min.   : 0.0000   Min.   :    0  
 Class :character                   1st Qu.:4.310           1st Qu.:  0.000   1st Qu.: 0.0000   1st Qu.: 1008  
 Mode  :character                   Median :4.808           Median :  0.000   Median : 0.0000   Median : 2316  
                                    Mean   :4.939           Mean   :  5.931   Mean   : 0.1779   Mean   : 3614  
 Lucerne.Root.LengthDensity.1. Lucerne.Root.LengthDensity.2. Lucerne.Root.LengthDensity.3. Lucerne.Root.LengthDensity.4.
 Min.   :0.000000              Min.   :0.0000000             Min.   :0.0000000             Min.   :0.0000000            
 1st Qu.:0.000561              1st Qu.:0.0004222             1st Qu.:0.0002453             1st Qu.:0.0001412            
 Median :0.001456              Median :0.0010220             Median :0.0006460             Median :0.0004311            
 Mean   :0.003661              Mean   :0.0016247             Mean   :0.0010904             Mean   :0.0007684            
 Lucerne.Root.LengthDensity.5. Lucerne.Root.LengthDensity.6. Lucerne.Root.LengthDensity.7. Lucerne.Root.LengthDensity.8.
 Min.   :0.000e+00             Min.   :0.0000                Min.   :0.0000                Min.   :0.000                
 1st Qu.:5.606e-05             1st Qu.:0.0000                1st Qu.:0.0000                1st Qu.:0.000                
 Median :3.537e-04             Median :0.0003                Median :0.0003                Median :0.000                
 Mean   :6.366e-04             Mean   :0.0006                Mean   :0.0006                Mean   :0.001                
 Lucerne.Root.LengthDensity.9. Lucerne.Root.LengthDensity.10. Lucerne.Root.LengthDensity.11.
 Min.   :0.000                 Min.   :0.000                  Min.   :0.000                 
 1st Qu.:0.000                 1st Qu.:0.000                  1st Qu.:0.000                 
 Median :0.000                 Median :0.000                  Median :0.000                 
 Mean   :0.001                 Mean   :0.000                  Mean   :0.000                 
 Lucerne.Root.LengthDensity.12. Lucerne.Root.LengthDensity.13. Lucerne.Root.LengthDensity.14.
 Min.   :0.000                  Min.   :0.000                  Min.   :0.000                 
 1st Qu.:0.000                  1st Qu.:0.000                  1st Qu.:0.000                 
 Median :0.000                  Median :0.000                  Median :0.000                 
 Mean   :0.000                  Mean   :0.000                  Mean   :0.000                 
 Lucerne.Root.LengthDensity.15. Lucerne.Root.LengthDensity.16. Lucerne.Root.LengthDensity.17.
 Min.   :0.000                  Min.   :0.000                  Min.   :0.000                 
 1st Qu.:0.000                  1st Qu.:0.000                  1st Qu.:0.000                 
 Median :0.000                  Median :0.000                  Median :0.000                 
 Mean   :0.000                  Mean   :0.000                  Mean   :0.000                 
 Lucerne.Root.LengthDensity.18. Lucerne.Root.LengthDensity.19. Lucerne.Root.LengthDensity.20.
 Min.   :0.000                  Min.   :0.000                  Min.   :0.000                 
 1st Qu.:0.000                  1st Qu.:0.000                  1st Qu.:0.000                 
 Median :0.000                  Median :0.000                  Median :0.000                 
 Mean   :0.000                  Mean   :0.000                  Mean   :0.000                 
 Lucerne.Root.LengthDensity.21. Lucerne.Root.LengthDensity.22. Lucerne.Root.LengthDensity.23. Soil.SoilWater.WaterTable
 Min.   :0.000                  Min.   :0.000                  Min.   :0.000                  Min.   :   0.763         
 1st Qu.:0.000                  1st Qu.:0.000                  1st Qu.:0.000                  1st Qu.:1500.000         
 Median :0.000                  Median :0.000                  Median :0.000                  Median :2300.000         
 Mean   :0.000                  Mean   :0.000                  Mean   :0.000                  Mean   :2043.145         
 Lucerne.AboveGround.Wt Lucerne.AboveGround.N Soil.SoilWater.ESW.1. Soil.SoilWater.ESW.2. Soil.SoilWater.ESW.3.
 Min.   :   0.0         Min.   : 0.000        Min.   : 0.00         Min.   : 0.000        Min.   : 0.00        
 1st Qu.: 100.8         1st Qu.: 1.709        1st Qu.: 6.85         1st Qu.: 8.134        1st Qu.: 6.00        
 Median : 231.6         Median : 4.422        Median :16.71         Median :19.774        Median :17.85        
 Mean   : 361.4         Mean   : 7.140        Mean   :15.63         Mean   :17.125        Mean   :16.00        
 Soil.SoilWater.ESW.4. Soil.SoilWater.ESW.5. Soil.SoilWater.ESW.6. Soil.SoilWater.ESW.7. Soil.SoilWater.ESW.8.
 Min.   : 0.000        Min.   :  0.000       Min.   : 0.000        Min.   : 0.000        Min.   : 0.00        
 1st Qu.: 3.823        1st Qu.:  2.194       1st Qu.: 1.203        1st Qu.: 2.039        1st Qu.: 3.33        
 Median :16.289        Median : 14.646       Median :15.028        Median :14.378        Median :14.72        
 Mean   :14.744        Mean   : 13.601       Mean   :14.014        Mean   :13.349        Mean   :14.69        
 Soil.SoilWater.ESW.9. Soil.SoilWater.ESW.10. Soil.SoilWater.ESW.11. Soil.SoilWater.ESW.12. Soil.SoilWater.ESW.13.
 Min.   : 0.000        Min.   : 0.000         Min.   : 0.000         Min.   : 0.000         Min.   : 0.000        
 1st Qu.: 2.195        1st Qu.: 1.617         1st Qu.: 2.878         1st Qu.: 8.027         1st Qu.: 7.445        
 Median :13.891        Median :13.651         Median :15.098         Median :16.565         Median :15.733        
 Mean   :13.528        Mean   :13.655         Mean   :14.100         Mean   :15.615         Mean   :14.553        
 Soil.SoilWater.ESW.14. Soil.SoilWater.ESW.15. Soil.SoilWater.ESW.16. Soil.SoilWater.ESW.17. Soil.SoilWater.ESW.18.
 Min.   : 0.000         Min.   : 0.000         Min.   : 0.00          Min.   : 0.00          Min.   : 0.00         
 1st Qu.: 7.172         1st Qu.: 8.141         1st Qu.:11.02          1st Qu.:14.00          1st Qu.:14.00         
 Median :16.238         Median :16.571         Median :16.94          Median :18.69          Median :18.63         
 Mean   :14.820         Mean   :15.538         Mean   :15.72          Mean   :16.90          Mean   :17.14         
 Soil.SoilWater.ESW.19. Soil.SoilWater.ESW.20. Soil.SoilWater.ESW.21. Soil.SoilWater.ESW.22. Soil.SoilWater.ESW.23.
 Min.   : 0.00          Min.   : 0.00          Min.   : 0.00          Min.   : 0.00          Min.   : 6.025        
 1st Qu.:14.00          1st Qu.:15.72          1st Qu.:14.02          1st Qu.: 8.32          1st Qu.:12.608        
 Median :18.90          Median :20.30          Median :18.98          Median :18.16          Median :21.281        
 Mean   :17.36          Mean   :18.30          Mean   :17.57          Mean   :16.26          Mean   :19.715        
  CheckpointID  SowingDate        Defoliation             FD              Factors          Soil.OutputLayers.SWmm.1.
 Min.   :1     Length:56703       Length:56703       Length:56703       Length:56703       Min.   :  3.00           
 1st Qu.:1     Class :character   Class :character   Class :character   Class :character   1st Qu.: 14.34           
 Median :1     Mode  :character   Mode  :character   Mode  :character   Mode  :character   Median : 21.67           
 Mean   :1                                                                                 Mean   : 25.46           
 Soil.OutputLayers.SWmm.2. Soil.OutputLayers.SWmm.3. Soil.OutputLayers.SWmm.4. Soil.OutputLayers.SWmm.5.
 Min.   : 21.00            Min.   : 42.09            Min.   :  0.00            Min.   : 51.35           
 1st Qu.: 29.67            1st Qu.: 55.27            1st Qu.: 52.00            1st Qu.: 86.27           
 Median : 63.91            Median : 71.42            Median : 80.01            Median :119.08           
 Mean   : 76.81            Mean   : 87.61            Mean   : 87.10            Mean   :117.11           
 Soil.OutputLayers.SWmm.6. Soil.OutputLayers.SWmm.7. Soil.OutputLayers.SWmm.8. Soil.OutputLayers.SW.1.
 Min.   : 54.79            Min.   : 57.97            Min.   : 60.89            Min.   :0.03           
 1st Qu.: 74.11            1st Qu.: 90.00            1st Qu.: 88.00            1st Qu.:0.07           
 Median : 93.97            Median : 99.01            Median : 92.08            Median :0.18           
 Mean   :105.21            Mean   :108.70            Mean   :137.56            Mean   :0.19           
 Soil.OutputLayers.SW.2. Soil.OutputLayers.SW.3. Soil.OutputLayers.SW.4. Soil.OutputLayers.SW.5. Soil.OutputLayers.SW.6.
 Min.   :0.05            Min.   :0.05            Min.   :0.00            Min.   :0.22            Min.   :0.19           
 1st Qu.:0.10            1st Qu.:0.16            1st Qu.:0.20            1st Qu.:0.24            1st Qu.:0.21           
 Median :0.23            Median :0.24            Median :0.25            Median :0.29            Median :0.28           
 Mean   :0.23            Mean   :0.24            Mean   :0.25            Mean   :0.30            Mean   :0.29           
 Soil.OutputLayers.SW.7. Soil.OutputLayers.SW.8. Soil.OutputLayers.SWmm.9. Soil.OutputLayers.SWmm.10.
 Min.   :0.22            Min.   :0.22            Min.   :63.01             Min.   :64.02             
 1st Qu.:0.23            1st Qu.:0.22            1st Qu.:63.35             1st Qu.:64.34             
 Median :0.29            Median :0.31            Median :63.71             Median :64.77             
 Mean   :0.30            Mean   :0.30            Mean   :63.67             Mean   :64.87             
 Soil.OutputLayers.SWmm.11. Soil.OutputLayers.SWmm.12. Soil.OutputLayers.SWmm.13. Soil.OutputLayers.SWmm.14.
 Min.   :64.00              Min.   :60.11              Min.   :58.00              Min.   :54.00             
 1st Qu.:64.00              1st Qu.:60.19              1st Qu.:58.00              1st Qu.:54.02             
 Median :64.00              Median :60.36              Median :58.00              Median :54.20             
 Mean   :64.36              Mean   :60.74              Mean   :58.00              Mean   :54.24             
 Soil.OutputLayers.SWmm.15. Soil.OutputLayers.SWmm.16. Soil.OutputLayers.SWmm.17. Soil.OutputLayers.SWmm.18.
 Min.   :54.00              Min.   :54.00              Min.   :54.53              Min.   :58.01             
 1st Qu.:54.02              1st Qu.:54.38              1st Qu.:54.68              1st Qu.:58.85             
 Median :54.08              Median :54.47              Median :54.82              Median :59.14             
 Mean   :54.06              Mean   :54.42              Mean   :55.14              Mean   :59.06             
 Soil.OutputLayers.SWmm.19. Soil.OutputLayers.SWmm.20. Soil.OutputLayers.SWmm.21. Soil.OutputLayers.SWmm.22.
 Min.   :60.91              Min.   :61.30              Min.   :59.00              Min.   :51.25             
 1st Qu.:61.73              1st Qu.:62.57              1st Qu.:60.73              1st Qu.:51.98             
 Median :62.52              Median :63.87              Median :63.27              Median :53.73             
 Mean   :62.49              Mean   :64.01              Mean   :63.77              Mean   :56.44             
 Soil.OutputLayers.SWmm.23. Soil.OutputLayers.SW.9. Soil.OutputLayers.SW.10. Soil.OutputLayers.SW.11.
 Min.   :0                  Min.   :0.32            Min.   :0.32             Min.   :0.32            
 1st Qu.:0                  1st Qu.:0.32            1st Qu.:0.32             1st Qu.:0.32            
 Median :0                  Median :0.32            Median :0.32             Median :0.32            
 Mean   :0                  Mean   :0.32            Mean   :0.32             Mean   :0.32            
 Soil.OutputLayers.SW.12. Soil.OutputLayers.SW.13. Soil.OutputLayers.SW.14. Soil.OutputLayers.SW.15.
 Min.   :0.30             Min.   :0.29             Min.   :0.27             Min.   :0.27            
 1st Qu.:0.30             1st Qu.:0.29             1st Qu.:0.27             1st Qu.:0.27            
 Median :0.30             Median :0.29             Median :0.27             Median :0.27            
 Mean   :0.30             Mean   :0.29             Mean   :0.27             Mean   :0.27            
 Soil.OutputLayers.SW.16. Soil.OutputLayers.SW.17. Soil.OutputLayers.SW.18. Soil.OutputLayers.SW.19.
 Min.   :0.27             Min.   :0.27             Min.   :0.29             Min.   :0.30            
 1st Qu.:0.27             1st Qu.:0.27             1st Qu.:0.29             1st Qu.:0.31            
 Median :0.27             Median :0.27             Median :0.30             Median :0.31            
 Mean   :0.27             Mean   :0.28             Mean   :0.30             Mean   :0.31            
 Soil.OutputLayers.SW.20. Soil.OutputLayers.SW.21. Soil.OutputLayers.SW.22. Soil.OutputLayers.SW.23.    SWmm.24.    
 Min.   :0.31             Min.   :0.30             Min.   :0.26             Min.   :0                Min.   :58.00  
 1st Qu.:0.31             1st Qu.:0.30             1st Qu.:0.26             1st Qu.:0                1st Qu.:58.78  
 Median :0.32             Median :0.32             Median :0.27             Median :0                Median :60.00  
 Mean   :0.32             Mean   :0.32             Mean   :0.28             Mean   :0                Mean   :59.43  
    SWmm.25.        SWmm.26.        SWmm.27.     Soil.NO3N.24.   Soil.NO3N.25.   Soil.NO3N.26.   Soil.NO3N.27.  
 Min.   :58.00   Min.   :58.00   Min.   :58.00   Min.   :0.04    Min.   :0.00    Min.   :0.00    Min.   :0.00   
 1st Qu.:60.00   1st Qu.:60.00   1st Qu.:60.00   1st Qu.:0.21    1st Qu.:0.02    1st Qu.:0.02    1st Qu.:0.02   
 Median :60.00   Median :60.00   Median :60.00   Median :0.28    Median :0.19    Median :0.19    Median :0.19   
 Mean   :59.76   Mean   :59.75   Mean   :59.75   Mean   :1.10    Mean   :1.03    Mean   :1.03    Mean   :1.04   
 Lucerne.Root.LengthDensity.24. Lucerne.Root.LengthDensity.25. Lucerne.Root.LengthDensity.26.
 Min.   :0                      Min.   :0                      Min.   :0                     
 1st Qu.:0                      1st Qu.:0                      1st Qu.:0                     
 Median :0                      Median :0                      Median :0                     
 Mean   :0                      Mean   :0                      Mean   :0                     
 Lucerne.Root.LengthDensity.27. Soil.SoilWater.ESW.24. Soil.SoilWater.ESW.25. Soil.SoilWater.ESW.26.
 Min.   :0                      Min.   :14.00          Min.   :14.00          Min.   :14.00         
 1st Qu.:0                      1st Qu.:14.78          1st Qu.:16.00          1st Qu.:16.00         
 Median :0                      Median :16.00          Median :16.00          Median :16.00         
 Mean   :0                      Mean   :15.43          Mean   :15.76          Mean   :15.75         
 Soil.SoilWater.ESW.27. Lucerne.Phenology.DaysAfterCutting.Value.. Lucerne.Phenology.FloweringDaysAfterCutting.Value..
 Min.   :14.00          Min.   :  0.00                             Min.   :  0.00                                     
 1st Qu.:16.00          1st Qu.: 13.00                             1st Qu.:  0.00                                     
 Median :16.00          Median : 30.00                             Median :  0.00                                     
 Mean   :15.75          Mean   : 33.22                             Mean   : 23.65                                     
   NodeNumber     Lucerne.Leaf.HeightFunction.DeltaHeight.Value.. Lucerne.Leaf.LAIFunction.Value..     Name          
 Min.   : 0.000   Min.   : 0.000                                  Min.   :0.05000                  Length:56703      
 1st Qu.: 2.688   1st Qu.: 2.020                                  1st Qu.:0.05159                  Class :character  
 Median : 6.317   Median : 5.416                                  Median :1.00000                  Mode  :character  
 Mean   : 8.911   Mean   : 8.122                                  Mean   :2.30092                                    
 [ reached getOption("max.print") -- omitted 3 rows ]
# myDb %>%
#   dplyr::select(Name) %>%
#   unique()

Prepare merge

Add info for merging

select variables that are for comparing with observed data

simD <- myDb %>%
  dplyr::select(Name,Clock.Today,LAI,SWC,Height,shootbiomass,RootWt, StemWt, LeafWt,NodeNumber) %>%
  tidyr::gather("Variable","Predicted",LAI:NodeNumber) %>%
  mutate(Name = as.factor(Name)) %>%
  mutate(Variable = as.factor(Variable)) %>%
  mutate(Clock.Today = ymd_hms(Clock.Today))
head(simD)
summary(simD)
                    Name         Clock.Today                          Variable        Predicted       
 RutherglenDefoliation: 20448   Min.   :1979-01-01 12:00:00   Height      : 56703   Min.   :    0.00  
 WarraSowingDateSD1   : 16016   1st Qu.:1998-11-20 12:00:00   LAI         : 56703   1st Qu.:   11.86  
 HudsonDefoliation    : 15984   Median :2001-11-30 12:00:00   LeafWt      : 56703   Median :  383.81  
 Iversen_8Waterdry    : 15152   Mean   :2004-04-22 12:38:26   NodeNumber  : 56703   Mean   : 1071.01  
 Iversen_8Waterirr    : 15152   3rd Qu.:2012-03-15 12:00:00   RootWt      : 56703   3rd Qu.: 1040.32  
 TamworthDefoliation  : 13208   Max.   :2018-01-16 12:00:00   shootbiomass: 56703   Max.   :29003.89  
 (Other)              :357664                                 (Other)     :113406                     
head(ObsH)
mergedf<-merge(obsHN,simD,by=c("Clock.Today","Name","Variable"))
summary(mergedf)
  Clock.Today                                     Name           Variable                                   ExpUnitCode 
 Min.   :1997-10-23   Iversen_121DefoliationLLFDFD5 :108   Height    :408   Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14  
 1st Qu.:2001-02-20   Iversen_91DefoliationLL       :107   Branch    :  0   Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12  
 Median :2002-10-25   Iversen_8Waterirr             : 68   Fraction  :  0   Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11  
 Mean   :2005-09-17   Iversen_9SowingDateSD1Waterirr: 67   HardStemWt:  0   Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11  
 3rd Qu.:2015-02-09   Iversen_9SowingDateSD2Waterirr: 25   LAI       :  0   Iversen_8WaterirrGs_5Rt_1             : 10  
 Max.   :2018-01-15   Iversen_9SowingDateSD3Waterirr: 18   LeafWt    :  0   Iversen_91DefoliationLLGs_2Rt_1       : 10  
                      (Other)                       : 15   (Other)   :  0   (Other)                               :340  
     Collection       Experiment.x Water.x   Defoliation.x  SowingDate.x   FD.x     GrowthSeason.x    Rotation.x   
 1997_2001: 68   Lincoln2000:131   dry:  0   HH:  0        No     :175   FD10:  0   Min.   :1.000   Min.   :1.000  
 2000_2002:125   Lincoln2015:108   irr:408   LL:408        no     :108   FD2 :  0   1st Qu.:1.000   1st Qu.:1.000  
 2002_2004:107   Lincoln2003: 54                           SD1    : 67   FD5 :408   Median :2.000   Median :3.000  
 2010_2012:  0   Lincoln2001: 35                           SD2    : 25              Mean   :2.105   Mean   :3.098  
 2014_2018:108   Lincoln2004: 31                           SD3    : 18              3rd Qu.:3.000   3rd Qu.:5.000  
 2014_2019:  0   Lincoln2002: 22                           SD4    : 15              Max.   :5.000   Max.   :7.000  
                 (Other)    : 27                           (Other):  0                                             
   StartDate             MidDate             FinishDate            Interval        VariableUnits       Time    
 Min.   :1997-10-07   Min.   :1997-10-28   Min.   :1997-11-19   Min.   :  0.00   %        :  0   12:00:00:408  
 1st Qu.:2001-01-24   1st Qu.:2001-02-13   1st Qu.:2001-03-23   1st Qu.: 14.75   cm       :233                 
 Median :2002-10-06   Median :2002-10-26   Median :2002-11-16   Median : 28.00   fractio0l:  0                 
 Mean   :2005-08-16   Mean   :2005-09-14   Mean   :2005-10-14   Mean   : 32.18   Fraction :  0                 
 3rd Qu.:2015-01-30   3rd Qu.:2015-02-19   3rd Qu.:2015-03-11   3rd Qu.: 41.00   kg/ha    :  0                 
 Max.   :2017-12-04   Max.   :2017-12-25   Max.   :2018-01-15   Max.   :116.00   m2/m2    :  0                 
                                                                                 mm       :175                 
    Observed          StdDEV       GrowthSeason1 Rotation1   Clock.Today1                      year           day       
 Min.   :  0.00   Min.   : 0.000   Gs_1:160      Rt_1:112   Min.   :1997-10-23 12:00:00   Min.   :1997   Min.   :  1.0  
 1st Qu.: 62.12   1st Qu.: 0.000   Gs_2:140      Rt_2: 80   1st Qu.:2001-02-20 12:00:00   1st Qu.:2001   1st Qu.: 63.0  
 Median :185.00   Median : 3.107   Gs_3: 54      Rt_3: 59   Median :2002-10-25 12:00:00   Median :2002   Median :138.5  
 Mean   :219.48   Mean   :12.001   Gs_4: 13      Rt_4: 50   Mean   :2005-09-17 22:25:44   Mean   :2005   Mean   :170.4  
 3rd Qu.:360.50   3rd Qu.:16.955   Gs_5: 41      Rt_5: 46   3rd Qu.:2015-02-10 06:00:00   3rd Qu.:2015   3rd Qu.:292.0  
 Max.   :798.00   Max.   :91.520   Gs_6:  0      Rt_6: 42   Max.   :2018-01-15 12:00:00   Max.   :2018   Max.   :365.0  
                  NA's   :229                    Rt_7: 19                                                               
      rain             maxt            mint             mean            radn            wind             vp       
 Min.   : 0.000   Min.   : 7.90   Min.   :-4.900   Min.   : 2.55   Min.   : 1.50   Min.   :0.700   Min.   : 5.10  
 1st Qu.: 0.000   1st Qu.:15.32   1st Qu.: 4.375   1st Qu.:10.40   1st Qu.: 9.70   1st Qu.:2.900   1st Qu.:10.00  
 Median : 0.000   Median :19.00   Median : 8.700   Median :13.20   Median :16.14   Median :3.900   Median :11.40  
 Mean   : 0.876   Mean   :19.17   Mean   : 7.853   Mean   :13.49   Mean   :16.73   Mean   :3.997   Mean   :11.77  
 3rd Qu.: 0.000   3rd Qu.:22.23   3rd Qu.:11.300   3rd Qu.:16.50   3rd Qu.:22.62   3rd Qu.:4.900   3rd Qu.:13.72  
 Max.   :31.800   Max.   :33.80   Max.   :20.600   Max.   :26.20   Max.   :33.40   Max.   :9.300   Max.   :22.00  
                                                                                                                  
       Pp              Tb        TTbeta            Tbb       TTbroken           TbF        TTfick      
 Min.   :10.02   Min.   :1   Min.   : 0.128   Min.   :1   Min.   : 1.841   Min.   :1   Min.   : 2.222  
 1st Qu.:12.21   1st Qu.:1   1st Qu.: 2.247   1st Qu.:1   1st Qu.: 6.822   1st Qu.:1   1st Qu.: 8.172  
 Median :14.27   Median :1   Median : 4.118   Median :1   Median : 9.015   Median :1   Median :10.646  
 Mean   :13.93   Mean   :1   Mean   : 5.812   Mean   :1   Mean   : 9.535   Mean   :1   Mean   :10.878  
 3rd Qu.:15.82   3rd Qu.:1   3rd Qu.: 8.085   3rd Qu.:1   3rd Qu.:12.040   3rd Qu.:1   3rd Qu.:13.588  
 Max.   :16.65   Max.   :1   Max.   :23.343   Max.   :1   Max.   :20.258   Max.   :1   Max.   :20.813  
                                                                                                       
                                   ExpName         Experiment.y Water.y   Defoliation.y  SowingDate.y   FD.y    
 Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14   Lincoln1997: 68   dry:  0   HH:  0        No     :175   FD10:  0  
 Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12   Lincoln2000:110   irr:408   LL:408        no     :108   FD2 :  0  
 Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11   Lincoln2001: 15             LS:  0        SD1    : 67   FD5 :408  
 Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11   Lincoln2002:107             SL:  0        SD2    : 25             
 Iversen_8WaterirrGs_5Rt_1             : 10   Lincoln2010:  0             SS:  0        SD3    : 18             
 Iversen_91DefoliationLLGs_2Rt_1       : 10   Lincoln2015:108                           SD4    : 15             
 (Other)                               :340                                             (Other):  0             
 GrowthSeason.y   Rotation.y   Tt_beta_sum        Tt_fick_sum       Tt_broken_sum           Ppm            Tmean       
 Gs_1:160       Rt_1   :112   Min.   :  0.2834   Min.   :   4.022   Min.   :   3.333   Min.   :10.25   Min.   : 7.024  
 Gs_2:140       Rt_2   : 80   1st Qu.: 60.8023   1st Qu.: 165.210   1st Qu.: 142.690   1st Qu.:11.99   1st Qu.:10.974  
 Gs_3: 54       Rt_3   : 59   Median :123.4401   Median : 299.224   Median : 258.979   Median :14.48   Median :13.963  
 Gs_4: 13       Rt_4   : 50   Mean   :157.2934   Mean   : 326.606   Mean   : 283.618   Mean   :13.84   Mean   :13.226  
 Gs_5: 41       Rt_5   : 46   3rd Qu.:218.2569   3rd Qu.: 453.159   3rd Qu.: 395.238   3rd Qu.:15.99   3rd Qu.:15.771  
 Gs_6:  0       Rt_6   : 42   Max.   :722.6708   Max.   :1186.654   Max.   :1051.876   Max.   :16.55   Max.   :19.327  
                (Other): 19                                                                                            
 GrowthRotation   Predicted    
 11     : 60    Min.   :  0.0  
 12     : 38    1st Qu.:103.4  
 21     : 24    Median :222.0  
 26     : 24    Mean   :266.7  
 13     : 23    3rd Qu.:403.0  
 22     : 23    Max.   :858.6  
 (Other):216                   
str(mergedf)
'data.frame':   408 obs. of  54 variables:
 $ Clock.Today   : Date, format: "1997-10-23" "1997-10-28" "1997-11-03" "1997-11-10" ...
 $ Name          : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 16 16 16 16 16 16 16 16 16 16 ...
 $ Variable      : Factor w/ 18 levels "Branch","Fraction",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ ExpUnitCode   : Factor w/ 263 levels "Iversen_121DefoliationHHFDFD5Gs_1Rt_2",..: 152 152 152 152 152 153 153 153 154 154 ...
 $ Collection    : Factor w/ 6 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Experiment.x  : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 2 2 ...
 $ Water.x       : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
 $ Defoliation.x : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
 $ SowingDate.x  : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ FD.x          : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
 $ GrowthSeason.x: int  2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation.x    : int  2 2 2 2 2 3 3 3 4 4 ...
 $ StartDate     : Date, format: "1997-10-07" "1997-10-07" "1997-10-07" "1997-10-07" ...
 $ MidDate       : Date, format: "1997-10-28" "1997-10-28" "1997-10-28" "1997-10-28" ...
 $ FinishDate    : Date, format: "1997-11-19" "1997-11-19" "1997-11-19" "1997-11-19" ...
 $ Interval      : int  16 21 27 34 41 19 28 33 22 30 ...
 $ VariableUnits : Factor w/ 7 levels "%","cm","fractio0l",..: 7 7 7 7 7 7 7 7 7 7 ...
 $ Time          : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
 $ Observed      : num  62.7 146.7 320.7 436.7 564.3 ...
 $ StdDEV        : num  0 0 0 0 0 0 0 0 0 0 ...
 $ GrowthSeason1 : Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation1     : Factor w/ 7 levels "Rt_1","Rt_2",..: 2 2 2 2 2 3 3 3 4 4 ...
 $ Clock.Today1  : POSIXct, format: "1997-10-23 12:00:00" "1997-10-28 12:00:00" "1997-11-03 12:00:00" "1997-11-10 12:00:00" ...
 $ year          : int  1997 1997 1997 1997 1997 1997 1997 1997 1998 1998 ...
 $ day           : int  296 301 307 314 321 343 352 357 15 23 ...
 $ rain          : num  0 0.7 0 0 0 0 0 0 0 0 ...
 $ maxt          : num  15.1 24.3 26 12 18.5 19.4 28 31.9 31.4 25.1 ...
 $ mint          : num  8.8 6.9 5.8 4.7 9.6 8 17 14.1 14.2 15.6 ...
 $ mean          : num  12 15.6 15.9 8.4 14.1 13.7 22.5 23 22.8 20.4 ...
 $ radn          : num  20.2 15.6 26.2 26 22.5 30.7 21.6 19.7 22.1 16 ...
 $ wind          : num  4.3 2.9 5.6 5.7 5.8 4.2 6.1 4.9 7.8 3.9 ...
 $ vp            : num  10.8 13.1 11.5 7.9 10.6 12.3 12.7 16.1 14.2 17.4 ...
 $ Pp            : num  14.6 14.9 15.2 15.5 15.8 ...
 $ Tb            : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTbeta        : num  2.652 8.148 9.211 0.944 4.723 ...
 $ Tbb           : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTbroken      : num  7.87 11.44 11.84 5.3 9.66 ...
 $ TbF           : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTfick        : num  9.49 12.74 13.02 6.4 11.33 ...
 $ ExpName       : Factor w/ 338 levels "Iversen_121DefoliationHHFDFD10Gs_1Rt_2",..: 171 171 171 171 171 172 172 172 173 173 ...
 $ Experiment.y  : Factor w/ 6 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Water.y       : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
 $ Defoliation.y : Factor w/ 5 levels "HH","LL","LS",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ SowingDate.y  : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ FD.y          : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
 $ GrowthSeason.y: Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation.y    : Factor w/ 10 levels "Rt_1","Rt_10",..: 3 3 3 3 3 4 4 4 5 5 ...
 $ Tt_beta_sum   : num  69.7 96.2 141.7 172.5 217.7 ...
 $ Tt_fick_sum   : num  166 220 295 363 447 ...
 $ Tt_broken_sum : num  142 189 256 315 388 ...
 $ Ppm           : num  14.9 14.9 14.9 14.9 14.9 ...
 $ Tmean         : num  13.2 13.2 13.2 13.2 13.2 ...
 $ GrowthRotation: Factor w/ 36 levels "11","12","13",..: 9 9 9 9 9 10 10 10 11 11 ...
 $ Predicted     : num  187 254 355 443 554 ...
mergedf

Node number

Time series

obs Vs Pre for each experiment

1997-2001

obsheight1<-ObsH%>%dplyr::filter(Name=="Iversen_8Waterirr")
  
 simD1<-simD%>%
   mutate(Clock.Today = ymd_hms(Clock.Today))%>%
   dplyr::filter(Variable=="Height")%>%
   dplyr::filter(Name=="Iversen_8Waterirr")
 str(simD1)
'data.frame':   1894 obs. of  4 variables:
 $ Name       : Factor w/ 63 levels "AshleyDeneSowingDateSD1",..: 35 35 35 35 35 35 35 35 35 35 ...
 $ Clock.Today: POSIXct, format: "2000-03-08 12:00:00" "1997-11-30 12:00:00" "1997-02-09 12:00:00" "1998-04-06 12:00:00" ...
 $ Variable   : Factor w/ 8 levels "Height","LAI",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Predicted  : num  668.9 203.4 894.4 243.1 25.7 ...
 simD1%>%
 ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
 facet_wrap(~Name,ncol = 2)+
 geom_point(data=obsheight1, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
 theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Plant height (mm)")+
 theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
 theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

2002-2004

obsheight2<-obsHN%>% 
  dplyr::filter(Name=="Iversen_91DefoliationLL")%>%
  dplyr::filter(Variable=="Height")
simD2<-simD%>%
  mutate(Clock.Today = ymd_hms(Clock.Today))%>%
  dplyr::filter(Variable=="Height")%>%
  dplyr::filter(Name=="Iversen_91DefoliationLL")%>%
   dplyr::filter(Clock.Today>"2002-06-01")
str(simD2)
'data.frame':   884 obs. of  4 variables:
 $ Name       : Factor w/ 63 levels "AshleyDeneSowingDateSD1",..: 36 36 36 36 36 36 36 36 36 36 ...
 $ Clock.Today: POSIXct, format: "2004-08-06 12:00:00" "2003-05-04 12:00:00" "2004-08-07 12:00:00" "2004-05-04 12:00:00" ...
 $ Variable   : Factor w/ 8 levels "Height","LAI",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Predicted  : num  92.86 231.16 95.46 8.71 328.66 ...
simD2%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
  facet_wrap(~Name,ncol = 1)+
  geom_point(data=obsheight2, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
  theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Plant height (mm)")+
  theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
  theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

NA
NA
obsheight2<-obsHN%>% 
  dplyr::filter(Name=="Iversen_121DefoliationLLFDFD5")%>%
  dplyr::filter(Variable=="Height")
simD2<-simD%>%
  mutate(Clock.Today = ymd_hms(Clock.Today))%>%
  dplyr::filter(Variable=="Height")%>%
  dplyr::filter(Name=="Iversen_121DefoliationLLFDFD5")
   
str(simD2)
'data.frame':   1197 obs. of  4 variables:
 $ Name       : Factor w/ 63 levels "AshleyDeneSowingDateSD1",..: 20 20 20 20 20 20 20 20 20 20 ...
 $ Clock.Today: POSIXct, format: "2014-10-27 12:00:00" "2014-12-09 12:00:00" "2014-10-11 12:00:00" "2014-10-16 12:00:00" ...
 $ Variable   : Factor w/ 8 levels "Height","LAI",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Predicted  : num  0 215.7 0 0 14.5 ...
simD2%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
  facet_wrap(~Name,ncol = 1)+
  geom_point(data=obsheight2, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
  theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Plant height (mm)")+
  theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
  theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

NA
NA

2000-2002

obsheight3<-obsHN%>%
  dplyr::filter(Collection=="2000_2002")%>%
  dplyr::filter(Variable=="Height")
  
simD3<-simD%>%
  mutate(Clock.Today = ymd_hms(Clock.Today))%>%
  dplyr::filter(Clock.Today>"2000-10-24 12:00:00")%>%
  dplyr::filter(Clock.Today<"2002-07-01 12:00:00")%>%
  dplyr::filter(Name!="Iversen_8Waterdry")%>%
  dplyr::filter(Name!="Iversen_8Waterirr")%>%
  dplyr::filter(Name!="Iversen_91DefoliationLL")%>%
  dplyr::filter(Name!="Iversen_91DefoliationLS")%>%
  dplyr::filter(Name!="Iversen_91DefoliationSL")%>%
  dplyr::filter(Name!="Iversen_91DefoliationSS")%>%
  dplyr::filter(Name!="Iversen_91DefoliationSS")%>%
  dplyr::filter(Name!="MooraDefoliation")%>%
  dplyr::filter(Name!="NekiaDefoliation")%>%
  dplyr::filter(Name!="QuairadingDefoliation")%>%
  dplyr::filter(Name!="RoseworthyWaterdry")%>%
  dplyr::filter(Name!="RoseworthyWaterirr")%>%
  dplyr::filter(Name!="Iversen_9SowingDateSD1Waterdry")%>%
  dplyr::filter(Name!="Iversen_9SowingDateSD2Waterdry")%>%
  dplyr::filter(Name!="Iversen_9SowingDateSD3Waterdry")%>%
  dplyr::filter(Name!="Iversen_9SowingDateSD4Waterdry")%>%
  dplyr::filter(Variable=="Height")
  str(simD3)
'data.frame':   2333 obs. of  4 variables:
 $ Name       : Factor w/ 63 levels "AshleyDeneSowingDateSD1",..: 41 41 41 41 41 41 41 41 41 41 ...
 $ Clock.Today: POSIXct, format: "2001-08-05 12:00:00" "2001-09-10 12:00:00" "2001-05-23 12:00:00" "2000-11-21 12:00:00" ...
 $ Variable   : Factor w/ 8 levels "Height","LAI",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Predicted  : num  50.6 169 72.1 0 67 ...
simD3%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
  facet_wrap(~Name,ncol = 2)+
  geom_point(data=obsheight3, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
  facet_wrap(~Name,ncol = 2)+
  theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Plant height (mm)")+
  theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
  theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

NA

Statistic and Graph

mergedf
summary(mergedf)
  Clock.Today                                     Name           Variable                                   ExpUnitCode 
 Min.   :1997-10-23   Iversen_121DefoliationLLFDFD5 :108   Height    :408   Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14  
 1st Qu.:2001-02-20   Iversen_91DefoliationLL       :107   Branch    :  0   Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12  
 Median :2002-10-25   Iversen_8Waterirr             : 68   Fraction  :  0   Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11  
 Mean   :2005-09-17   Iversen_9SowingDateSD1Waterirr: 67   HardStemWt:  0   Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11  
 3rd Qu.:2015-02-09   Iversen_9SowingDateSD2Waterirr: 25   LAI       :  0   Iversen_8WaterirrGs_5Rt_1             : 10  
 Max.   :2018-01-15   Iversen_9SowingDateSD3Waterirr: 18   LeafWt    :  0   Iversen_91DefoliationLLGs_2Rt_1       : 10  
                      (Other)                       : 15   (Other)   :  0   (Other)                               :340  
     Collection       Experiment.x Water.x   Defoliation.x  SowingDate.x   FD.x     GrowthSeason.x    Rotation.x   
 1997_2001: 68   Lincoln2000:131   dry:  0   HH:  0        No     :175   FD10:  0   Min.   :1.000   Min.   :1.000  
 2000_2002:125   Lincoln2015:108   irr:408   LL:408        no     :108   FD2 :  0   1st Qu.:1.000   1st Qu.:1.000  
 2002_2004:107   Lincoln2003: 54                           SD1    : 67   FD5 :408   Median :2.000   Median :3.000  
 2010_2012:  0   Lincoln2001: 35                           SD2    : 25              Mean   :2.105   Mean   :3.098  
 2014_2018:108   Lincoln2004: 31                           SD3    : 18              3rd Qu.:3.000   3rd Qu.:5.000  
 2014_2019:  0   Lincoln2002: 22                           SD4    : 15              Max.   :5.000   Max.   :7.000  
                 (Other)    : 27                           (Other):  0                                             
   StartDate             MidDate             FinishDate            Interval        VariableUnits       Time    
 Min.   :1997-10-07   Min.   :1997-10-28   Min.   :1997-11-19   Min.   :  0.00   %        :  0   12:00:00:408  
 1st Qu.:2001-01-24   1st Qu.:2001-02-13   1st Qu.:2001-03-23   1st Qu.: 14.75   cm       :233                 
 Median :2002-10-06   Median :2002-10-26   Median :2002-11-16   Median : 28.00   fractio0l:  0                 
 Mean   :2005-08-16   Mean   :2005-09-14   Mean   :2005-10-14   Mean   : 32.18   Fraction :  0                 
 3rd Qu.:2015-01-30   3rd Qu.:2015-02-19   3rd Qu.:2015-03-11   3rd Qu.: 41.00   kg/ha    :  0                 
 Max.   :2017-12-04   Max.   :2017-12-25   Max.   :2018-01-15   Max.   :116.00   m2/m2    :  0                 
                                                                                 mm       :175                 
    Observed          StdDEV       GrowthSeason1 Rotation1   Clock.Today1                      year           day       
 Min.   :  0.00   Min.   : 0.000   Gs_1:160      Rt_1:112   Min.   :1997-10-23 12:00:00   Min.   :1997   Min.   :  1.0  
 1st Qu.: 62.12   1st Qu.: 0.000   Gs_2:140      Rt_2: 80   1st Qu.:2001-02-20 12:00:00   1st Qu.:2001   1st Qu.: 63.0  
 Median :185.00   Median : 3.107   Gs_3: 54      Rt_3: 59   Median :2002-10-25 12:00:00   Median :2002   Median :138.5  
 Mean   :219.48   Mean   :12.001   Gs_4: 13      Rt_4: 50   Mean   :2005-09-17 22:25:44   Mean   :2005   Mean   :170.4  
 3rd Qu.:360.50   3rd Qu.:16.955   Gs_5: 41      Rt_5: 46   3rd Qu.:2015-02-10 06:00:00   3rd Qu.:2015   3rd Qu.:292.0  
 Max.   :798.00   Max.   :91.520   Gs_6:  0      Rt_6: 42   Max.   :2018-01-15 12:00:00   Max.   :2018   Max.   :365.0  
                  NA's   :229                    Rt_7: 19                                                               
      rain             maxt            mint             mean            radn            wind             vp       
 Min.   : 0.000   Min.   : 7.90   Min.   :-4.900   Min.   : 2.55   Min.   : 1.50   Min.   :0.700   Min.   : 5.10  
 1st Qu.: 0.000   1st Qu.:15.32   1st Qu.: 4.375   1st Qu.:10.40   1st Qu.: 9.70   1st Qu.:2.900   1st Qu.:10.00  
 Median : 0.000   Median :19.00   Median : 8.700   Median :13.20   Median :16.14   Median :3.900   Median :11.40  
 Mean   : 0.876   Mean   :19.17   Mean   : 7.853   Mean   :13.49   Mean   :16.73   Mean   :3.997   Mean   :11.77  
 3rd Qu.: 0.000   3rd Qu.:22.23   3rd Qu.:11.300   3rd Qu.:16.50   3rd Qu.:22.62   3rd Qu.:4.900   3rd Qu.:13.72  
 Max.   :31.800   Max.   :33.80   Max.   :20.600   Max.   :26.20   Max.   :33.40   Max.   :9.300   Max.   :22.00  
                                                                                                                  
       Pp              Tb        TTbeta            Tbb       TTbroken           TbF        TTfick      
 Min.   :10.02   Min.   :1   Min.   : 0.128   Min.   :1   Min.   : 1.841   Min.   :1   Min.   : 2.222  
 1st Qu.:12.21   1st Qu.:1   1st Qu.: 2.247   1st Qu.:1   1st Qu.: 6.822   1st Qu.:1   1st Qu.: 8.172  
 Median :14.27   Median :1   Median : 4.118   Median :1   Median : 9.015   Median :1   Median :10.646  
 Mean   :13.93   Mean   :1   Mean   : 5.812   Mean   :1   Mean   : 9.535   Mean   :1   Mean   :10.878  
 3rd Qu.:15.82   3rd Qu.:1   3rd Qu.: 8.085   3rd Qu.:1   3rd Qu.:12.040   3rd Qu.:1   3rd Qu.:13.588  
 Max.   :16.65   Max.   :1   Max.   :23.343   Max.   :1   Max.   :20.258   Max.   :1   Max.   :20.813  
                                                                                                       
                                   ExpName         Experiment.y Water.y   Defoliation.y  SowingDate.y   FD.y    
 Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14   Lincoln1997: 68   dry:  0   HH:  0        No     :175   FD10:  0  
 Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12   Lincoln2000:110   irr:408   LL:408        no     :108   FD2 :  0  
 Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11   Lincoln2001: 15             LS:  0        SD1    : 67   FD5 :408  
 Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11   Lincoln2002:107             SL:  0        SD2    : 25             
 Iversen_8WaterirrGs_5Rt_1             : 10   Lincoln2010:  0             SS:  0        SD3    : 18             
 Iversen_91DefoliationLLGs_2Rt_1       : 10   Lincoln2015:108                           SD4    : 15             
 (Other)                               :340                                             (Other):  0             
 GrowthSeason.y   Rotation.y   Tt_beta_sum        Tt_fick_sum       Tt_broken_sum           Ppm            Tmean       
 Gs_1:160       Rt_1   :112   Min.   :  0.2834   Min.   :   4.022   Min.   :   3.333   Min.   :10.25   Min.   : 7.024  
 Gs_2:140       Rt_2   : 80   1st Qu.: 60.8023   1st Qu.: 165.210   1st Qu.: 142.690   1st Qu.:11.99   1st Qu.:10.974  
 Gs_3: 54       Rt_3   : 59   Median :123.4401   Median : 299.224   Median : 258.979   Median :14.48   Median :13.963  
 Gs_4: 13       Rt_4   : 50   Mean   :157.2934   Mean   : 326.606   Mean   : 283.618   Mean   :13.84   Mean   :13.226  
 Gs_5: 41       Rt_5   : 46   3rd Qu.:218.2569   3rd Qu.: 453.159   3rd Qu.: 395.238   3rd Qu.:15.99   3rd Qu.:15.771  
 Gs_6:  0       Rt_6   : 42   Max.   :722.6708   Max.   :1186.654   Max.   :1051.876   Max.   :16.55   Max.   :19.327  
                (Other): 19                                                                                            
 GrowthRotation   Predicted    
 11     : 60    Min.   :  0.0  
 12     : 38    1st Qu.:103.4  
 21     : 24    Median :222.0  
 26     : 24    Mean   :266.7  
 13     : 23    3rd Qu.:403.0  
 22     : 23    Max.   :858.6  
 (Other):216                   
str(mergedf)
'data.frame':   408 obs. of  54 variables:
 $ Clock.Today   : Date, format: "1997-10-23" "1997-10-28" "1997-11-03" "1997-11-10" ...
 $ Name          : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 16 16 16 16 16 16 16 16 16 16 ...
 $ Variable      : Factor w/ 18 levels "Branch","Fraction",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ ExpUnitCode   : Factor w/ 263 levels "Iversen_121DefoliationHHFDFD5Gs_1Rt_2",..: 152 152 152 152 152 153 153 153 154 154 ...
 $ Collection    : Factor w/ 6 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Experiment.x  : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 2 2 ...
 $ Water.x       : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
 $ Defoliation.x : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
 $ SowingDate.x  : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ FD.x          : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
 $ GrowthSeason.x: int  2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation.x    : int  2 2 2 2 2 3 3 3 4 4 ...
 $ StartDate     : Date, format: "1997-10-07" "1997-10-07" "1997-10-07" "1997-10-07" ...
 $ MidDate       : Date, format: "1997-10-28" "1997-10-28" "1997-10-28" "1997-10-28" ...
 $ FinishDate    : Date, format: "1997-11-19" "1997-11-19" "1997-11-19" "1997-11-19" ...
 $ Interval      : int  16 21 27 34 41 19 28 33 22 30 ...
 $ VariableUnits : Factor w/ 7 levels "%","cm","fractio0l",..: 7 7 7 7 7 7 7 7 7 7 ...
 $ Time          : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
 $ Observed      : num  62.7 146.7 320.7 436.7 564.3 ...
 $ StdDEV        : num  0 0 0 0 0 0 0 0 0 0 ...
 $ GrowthSeason1 : Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation1     : Factor w/ 7 levels "Rt_1","Rt_2",..: 2 2 2 2 2 3 3 3 4 4 ...
 $ Clock.Today1  : POSIXct, format: "1997-10-23 12:00:00" "1997-10-28 12:00:00" "1997-11-03 12:00:00" "1997-11-10 12:00:00" ...
 $ year          : int  1997 1997 1997 1997 1997 1997 1997 1997 1998 1998 ...
 $ day           : int  296 301 307 314 321 343 352 357 15 23 ...
 $ rain          : num  0 0.7 0 0 0 0 0 0 0 0 ...
 $ maxt          : num  15.1 24.3 26 12 18.5 19.4 28 31.9 31.4 25.1 ...
 $ mint          : num  8.8 6.9 5.8 4.7 9.6 8 17 14.1 14.2 15.6 ...
 $ mean          : num  12 15.6 15.9 8.4 14.1 13.7 22.5 23 22.8 20.4 ...
 $ radn          : num  20.2 15.6 26.2 26 22.5 30.7 21.6 19.7 22.1 16 ...
 $ wind          : num  4.3 2.9 5.6 5.7 5.8 4.2 6.1 4.9 7.8 3.9 ...
 $ vp            : num  10.8 13.1 11.5 7.9 10.6 12.3 12.7 16.1 14.2 17.4 ...
 $ Pp            : num  14.6 14.9 15.2 15.5 15.8 ...
 $ Tb            : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTbeta        : num  2.652 8.148 9.211 0.944 4.723 ...
 $ Tbb           : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTbroken      : num  7.87 11.44 11.84 5.3 9.66 ...
 $ TbF           : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTfick        : num  9.49 12.74 13.02 6.4 11.33 ...
 $ ExpName       : Factor w/ 338 levels "Iversen_121DefoliationHHFDFD10Gs_1Rt_2",..: 171 171 171 171 171 172 172 172 173 173 ...
 $ Experiment.y  : Factor w/ 6 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Water.y       : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
 $ Defoliation.y : Factor w/ 5 levels "HH","LL","LS",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ SowingDate.y  : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ FD.y          : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
 $ GrowthSeason.y: Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation.y    : Factor w/ 10 levels "Rt_1","Rt_10",..: 3 3 3 3 3 4 4 4 5 5 ...
 $ Tt_beta_sum   : num  69.7 96.2 141.7 172.5 217.7 ...
 $ Tt_fick_sum   : num  166 220 295 363 447 ...
 $ Tt_broken_sum : num  142 189 256 315 388 ...
 $ Ppm           : num  14.9 14.9 14.9 14.9 14.9 ...
 $ Tmean         : num  13.2 13.2 13.2 13.2 13.2 ...
 $ GrowthRotation: Factor w/ 36 levels "11","12","13",..: 9 9 9 9 9 10 10 10 11 11 ...
 $ Predicted     : num  187 254 355 443 554 ...
mergedf %>%
    dplyr::filter(Variable== "Height") %>% 
  ggplot(aes(x=Observed, y= Predicted, 
          colour= factor(Name))) +
  geom_point(size=2)+theme_bw()+
  geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
  geom_abline(intercept = 0, slope = 1) +
  coord_fixed(ratio = 1)+
  ggtitle("Plant height")+
  facet_wrap(~Collection, ncol = 4)+
  theme(legend.title=element_blank())+xlab("Observed")+ylab("Predicted")+
  theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
  theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

2002-2004

mergedf
summary(mergedf)
  Clock.Today                                     Name           Variable                                   ExpUnitCode 
 Min.   :1997-10-23   Iversen_121DefoliationLLFDFD5 :108   Height    :408   Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14  
 1st Qu.:2001-02-20   Iversen_91DefoliationLL       :107   Branch    :  0   Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12  
 Median :2002-10-25   Iversen_8Waterirr             : 68   Fraction  :  0   Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11  
 Mean   :2005-09-17   Iversen_9SowingDateSD1Waterirr: 67   HardStemWt:  0   Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11  
 3rd Qu.:2015-02-09   Iversen_9SowingDateSD2Waterirr: 25   LAI       :  0   Iversen_8WaterirrGs_5Rt_1             : 10  
 Max.   :2018-01-15   Iversen_9SowingDateSD3Waterirr: 18   LeafWt    :  0   Iversen_91DefoliationLLGs_2Rt_1       : 10  
                      (Other)                       : 15   (Other)   :  0   (Other)                               :340  
     Collection       Experiment.x Water.x   Defoliation.x  SowingDate.x   FD.x     GrowthSeason.x    Rotation.x   
 1997_2001: 68   Lincoln2000:131   dry:  0   HH:  0        No     :175   FD10:  0   Min.   :1.000   Min.   :1.000  
 2000_2002:125   Lincoln2015:108   irr:408   LL:408        no     :108   FD2 :  0   1st Qu.:1.000   1st Qu.:1.000  
 2002_2004:107   Lincoln2003: 54                           SD1    : 67   FD5 :408   Median :2.000   Median :3.000  
 2010_2012:  0   Lincoln2001: 35                           SD2    : 25              Mean   :2.105   Mean   :3.098  
 2014_2018:108   Lincoln2004: 31                           SD3    : 18              3rd Qu.:3.000   3rd Qu.:5.000  
 2014_2019:  0   Lincoln2002: 22                           SD4    : 15              Max.   :5.000   Max.   :7.000  
                 (Other)    : 27                           (Other):  0                                             
   StartDate             MidDate             FinishDate            Interval        VariableUnits       Time    
 Min.   :1997-10-07   Min.   :1997-10-28   Min.   :1997-11-19   Min.   :  0.00   %        :  0   12:00:00:408  
 1st Qu.:2001-01-24   1st Qu.:2001-02-13   1st Qu.:2001-03-23   1st Qu.: 14.75   cm       :233                 
 Median :2002-10-06   Median :2002-10-26   Median :2002-11-16   Median : 28.00   fractio0l:  0                 
 Mean   :2005-08-16   Mean   :2005-09-14   Mean   :2005-10-14   Mean   : 32.18   Fraction :  0                 
 3rd Qu.:2015-01-30   3rd Qu.:2015-02-19   3rd Qu.:2015-03-11   3rd Qu.: 41.00   kg/ha    :  0                 
 Max.   :2017-12-04   Max.   :2017-12-25   Max.   :2018-01-15   Max.   :116.00   m2/m2    :  0                 
                                                                                 mm       :175                 
    Observed          StdDEV       GrowthSeason1 Rotation1   Clock.Today1                      year           day       
 Min.   :  0.00   Min.   : 0.000   Gs_1:160      Rt_1:112   Min.   :1997-10-23 12:00:00   Min.   :1997   Min.   :  1.0  
 1st Qu.: 62.12   1st Qu.: 0.000   Gs_2:140      Rt_2: 80   1st Qu.:2001-02-20 12:00:00   1st Qu.:2001   1st Qu.: 63.0  
 Median :185.00   Median : 3.107   Gs_3: 54      Rt_3: 59   Median :2002-10-25 12:00:00   Median :2002   Median :138.5  
 Mean   :219.48   Mean   :12.001   Gs_4: 13      Rt_4: 50   Mean   :2005-09-17 22:25:44   Mean   :2005   Mean   :170.4  
 3rd Qu.:360.50   3rd Qu.:16.955   Gs_5: 41      Rt_5: 46   3rd Qu.:2015-02-10 06:00:00   3rd Qu.:2015   3rd Qu.:292.0  
 Max.   :798.00   Max.   :91.520   Gs_6:  0      Rt_6: 42   Max.   :2018-01-15 12:00:00   Max.   :2018   Max.   :365.0  
                  NA's   :229                    Rt_7: 19                                                               
      rain             maxt            mint             mean            radn            wind             vp       
 Min.   : 0.000   Min.   : 7.90   Min.   :-4.900   Min.   : 2.55   Min.   : 1.50   Min.   :0.700   Min.   : 5.10  
 1st Qu.: 0.000   1st Qu.:15.32   1st Qu.: 4.375   1st Qu.:10.40   1st Qu.: 9.70   1st Qu.:2.900   1st Qu.:10.00  
 Median : 0.000   Median :19.00   Median : 8.700   Median :13.20   Median :16.14   Median :3.900   Median :11.40  
 Mean   : 0.876   Mean   :19.17   Mean   : 7.853   Mean   :13.49   Mean   :16.73   Mean   :3.997   Mean   :11.77  
 3rd Qu.: 0.000   3rd Qu.:22.23   3rd Qu.:11.300   3rd Qu.:16.50   3rd Qu.:22.62   3rd Qu.:4.900   3rd Qu.:13.72  
 Max.   :31.800   Max.   :33.80   Max.   :20.600   Max.   :26.20   Max.   :33.40   Max.   :9.300   Max.   :22.00  
                                                                                                                  
       Pp              Tb        TTbeta            Tbb       TTbroken           TbF        TTfick      
 Min.   :10.02   Min.   :1   Min.   : 0.128   Min.   :1   Min.   : 1.841   Min.   :1   Min.   : 2.222  
 1st Qu.:12.21   1st Qu.:1   1st Qu.: 2.247   1st Qu.:1   1st Qu.: 6.822   1st Qu.:1   1st Qu.: 8.172  
 Median :14.27   Median :1   Median : 4.118   Median :1   Median : 9.015   Median :1   Median :10.646  
 Mean   :13.93   Mean   :1   Mean   : 5.812   Mean   :1   Mean   : 9.535   Mean   :1   Mean   :10.878  
 3rd Qu.:15.82   3rd Qu.:1   3rd Qu.: 8.085   3rd Qu.:1   3rd Qu.:12.040   3rd Qu.:1   3rd Qu.:13.588  
 Max.   :16.65   Max.   :1   Max.   :23.343   Max.   :1   Max.   :20.258   Max.   :1   Max.   :20.813  
                                                                                                       
                                   ExpName         Experiment.y Water.y   Defoliation.y  SowingDate.y   FD.y    
 Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14   Lincoln1997: 68   dry:  0   HH:  0        No     :175   FD10:  0  
 Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12   Lincoln2000:110   irr:408   LL:408        no     :108   FD2 :  0  
 Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11   Lincoln2001: 15             LS:  0        SD1    : 67   FD5 :408  
 Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11   Lincoln2002:107             SL:  0        SD2    : 25             
 Iversen_8WaterirrGs_5Rt_1             : 10   Lincoln2010:  0             SS:  0        SD3    : 18             
 Iversen_91DefoliationLLGs_2Rt_1       : 10   Lincoln2015:108                           SD4    : 15             
 (Other)                               :340                                             (Other):  0             
 GrowthSeason.y   Rotation.y   Tt_beta_sum        Tt_fick_sum       Tt_broken_sum           Ppm            Tmean       
 Gs_1:160       Rt_1   :112   Min.   :  0.2834   Min.   :   4.022   Min.   :   3.333   Min.   :10.25   Min.   : 7.024  
 Gs_2:140       Rt_2   : 80   1st Qu.: 60.8023   1st Qu.: 165.210   1st Qu.: 142.690   1st Qu.:11.99   1st Qu.:10.974  
 Gs_3: 54       Rt_3   : 59   Median :123.4401   Median : 299.224   Median : 258.979   Median :14.48   Median :13.963  
 Gs_4: 13       Rt_4   : 50   Mean   :157.2934   Mean   : 326.606   Mean   : 283.618   Mean   :13.84   Mean   :13.226  
 Gs_5: 41       Rt_5   : 46   3rd Qu.:218.2569   3rd Qu.: 453.159   3rd Qu.: 395.238   3rd Qu.:15.99   3rd Qu.:15.771  
 Gs_6:  0       Rt_6   : 42   Max.   :722.6708   Max.   :1186.654   Max.   :1051.876   Max.   :16.55   Max.   :19.327  
                (Other): 19                                                                                            
 GrowthRotation   Predicted    
 11     : 60    Min.   :  0.0  
 12     : 38    1st Qu.:103.4  
 21     : 24    Median :222.0  
 26     : 24    Mean   :266.7  
 13     : 23    3rd Qu.:403.0  
 22     : 23    Max.   :858.6  
 (Other):216                   
str(mergedf)
'data.frame':   408 obs. of  54 variables:
 $ Clock.Today   : Date, format: "1997-10-23" "1997-10-28" "1997-11-03" "1997-11-10" ...
 $ Name          : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 16 16 16 16 16 16 16 16 16 16 ...
 $ Variable      : Factor w/ 18 levels "Branch","Fraction",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ ExpUnitCode   : Factor w/ 263 levels "Iversen_121DefoliationHHFDFD5Gs_1Rt_2",..: 152 152 152 152 152 153 153 153 154 154 ...
 $ Collection    : Factor w/ 6 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Experiment.x  : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 2 2 ...
 $ Water.x       : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
 $ Defoliation.x : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
 $ SowingDate.x  : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ FD.x          : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
 $ GrowthSeason.x: int  2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation.x    : int  2 2 2 2 2 3 3 3 4 4 ...
 $ StartDate     : Date, format: "1997-10-07" "1997-10-07" "1997-10-07" "1997-10-07" ...
 $ MidDate       : Date, format: "1997-10-28" "1997-10-28" "1997-10-28" "1997-10-28" ...
 $ FinishDate    : Date, format: "1997-11-19" "1997-11-19" "1997-11-19" "1997-11-19" ...
 $ Interval      : int  16 21 27 34 41 19 28 33 22 30 ...
 $ VariableUnits : Factor w/ 7 levels "%","cm","fractio0l",..: 7 7 7 7 7 7 7 7 7 7 ...
 $ Time          : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
 $ Observed      : num  62.7 146.7 320.7 436.7 564.3 ...
 $ StdDEV        : num  0 0 0 0 0 0 0 0 0 0 ...
 $ GrowthSeason1 : Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation1     : Factor w/ 7 levels "Rt_1","Rt_2",..: 2 2 2 2 2 3 3 3 4 4 ...
 $ Clock.Today1  : POSIXct, format: "1997-10-23 12:00:00" "1997-10-28 12:00:00" "1997-11-03 12:00:00" "1997-11-10 12:00:00" ...
 $ year          : int  1997 1997 1997 1997 1997 1997 1997 1997 1998 1998 ...
 $ day           : int  296 301 307 314 321 343 352 357 15 23 ...
 $ rain          : num  0 0.7 0 0 0 0 0 0 0 0 ...
 $ maxt          : num  15.1 24.3 26 12 18.5 19.4 28 31.9 31.4 25.1 ...
 $ mint          : num  8.8 6.9 5.8 4.7 9.6 8 17 14.1 14.2 15.6 ...
 $ mean          : num  12 15.6 15.9 8.4 14.1 13.7 22.5 23 22.8 20.4 ...
 $ radn          : num  20.2 15.6 26.2 26 22.5 30.7 21.6 19.7 22.1 16 ...
 $ wind          : num  4.3 2.9 5.6 5.7 5.8 4.2 6.1 4.9 7.8 3.9 ...
 $ vp            : num  10.8 13.1 11.5 7.9 10.6 12.3 12.7 16.1 14.2 17.4 ...
 $ Pp            : num  14.6 14.9 15.2 15.5 15.8 ...
 $ Tb            : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTbeta        : num  2.652 8.148 9.211 0.944 4.723 ...
 $ Tbb           : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTbroken      : num  7.87 11.44 11.84 5.3 9.66 ...
 $ TbF           : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTfick        : num  9.49 12.74 13.02 6.4 11.33 ...
 $ ExpName       : Factor w/ 338 levels "Iversen_121DefoliationHHFDFD10Gs_1Rt_2",..: 171 171 171 171 171 172 172 172 173 173 ...
 $ Experiment.y  : Factor w/ 6 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Water.y       : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
 $ Defoliation.y : Factor w/ 5 levels "HH","LL","LS",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ SowingDate.y  : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ FD.y          : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
 $ GrowthSeason.y: Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation.y    : Factor w/ 10 levels "Rt_1","Rt_10",..: 3 3 3 3 3 4 4 4 5 5 ...
 $ Tt_beta_sum   : num  69.7 96.2 141.7 172.5 217.7 ...
 $ Tt_fick_sum   : num  166 220 295 363 447 ...
 $ Tt_broken_sum : num  142 189 256 315 388 ...
 $ Ppm           : num  14.9 14.9 14.9 14.9 14.9 ...
 $ Tmean         : num  13.2 13.2 13.2 13.2 13.2 ...
 $ GrowthRotation: Factor w/ 36 levels "11","12","13",..: 9 9 9 9 9 10 10 10 11 11 ...
 $ Predicted     : num  187 254 355 443 554 ...
mergedf %>%
    dplyr::filter(Collection=="2002_2004")%>%
  ggplot(aes(x=Observed, y= Predicted, 
          colour= factor(Name))) +
  geom_point(size=3)+theme_bw()+
  geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
  geom_abline(intercept = 0, slope = 1) +
  coord_fixed(ratio = 1)+
  ggtitle("Plant height")+
  facet_grid(GrowthSeason.x~Rotation.x)+
  theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
  theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
  theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

2000-2002

mergedf
summary(mergedf)
  Clock.Today                                     Name           Variable                                   ExpUnitCode 
 Min.   :1997-10-23   Iversen_121DefoliationLLFDFD5 :108   Height    :408   Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14  
 1st Qu.:2001-02-20   Iversen_91DefoliationLL       :107   Branch    :  0   Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12  
 Median :2002-10-25   Iversen_8Waterirr             : 68   Fraction  :  0   Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11  
 Mean   :2005-09-17   Iversen_9SowingDateSD1Waterirr: 67   HardStemWt:  0   Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11  
 3rd Qu.:2015-02-09   Iversen_9SowingDateSD2Waterirr: 25   LAI       :  0   Iversen_8WaterirrGs_5Rt_1             : 10  
 Max.   :2018-01-15   Iversen_9SowingDateSD3Waterirr: 18   LeafWt    :  0   Iversen_91DefoliationLLGs_2Rt_1       : 10  
                      (Other)                       : 15   (Other)   :  0   (Other)                               :340  
     Collection       Experiment.x Water.x   Defoliation.x  SowingDate.x   FD.x     GrowthSeason.x    Rotation.x   
 1997_2001: 68   Lincoln2000:131   dry:  0   HH:  0        No     :175   FD10:  0   Min.   :1.000   Min.   :1.000  
 2000_2002:125   Lincoln2015:108   irr:408   LL:408        no     :108   FD2 :  0   1st Qu.:1.000   1st Qu.:1.000  
 2002_2004:107   Lincoln2003: 54                           SD1    : 67   FD5 :408   Median :2.000   Median :3.000  
 2010_2012:  0   Lincoln2001: 35                           SD2    : 25              Mean   :2.105   Mean   :3.098  
 2014_2018:108   Lincoln2004: 31                           SD3    : 18              3rd Qu.:3.000   3rd Qu.:5.000  
 2014_2019:  0   Lincoln2002: 22                           SD4    : 15              Max.   :5.000   Max.   :7.000  
                 (Other)    : 27                           (Other):  0                                             
   StartDate             MidDate             FinishDate            Interval        VariableUnits       Time    
 Min.   :1997-10-07   Min.   :1997-10-28   Min.   :1997-11-19   Min.   :  0.00   %        :  0   12:00:00:408  
 1st Qu.:2001-01-24   1st Qu.:2001-02-13   1st Qu.:2001-03-23   1st Qu.: 14.75   cm       :233                 
 Median :2002-10-06   Median :2002-10-26   Median :2002-11-16   Median : 28.00   fractio0l:  0                 
 Mean   :2005-08-16   Mean   :2005-09-14   Mean   :2005-10-14   Mean   : 32.18   Fraction :  0                 
 3rd Qu.:2015-01-30   3rd Qu.:2015-02-19   3rd Qu.:2015-03-11   3rd Qu.: 41.00   kg/ha    :  0                 
 Max.   :2017-12-04   Max.   :2017-12-25   Max.   :2018-01-15   Max.   :116.00   m2/m2    :  0                 
                                                                                 mm       :175                 
    Observed          StdDEV       GrowthSeason1 Rotation1   Clock.Today1                      year           day       
 Min.   :  0.00   Min.   : 0.000   Gs_1:160      Rt_1:112   Min.   :1997-10-23 12:00:00   Min.   :1997   Min.   :  1.0  
 1st Qu.: 62.12   1st Qu.: 0.000   Gs_2:140      Rt_2: 80   1st Qu.:2001-02-20 12:00:00   1st Qu.:2001   1st Qu.: 63.0  
 Median :185.00   Median : 3.107   Gs_3: 54      Rt_3: 59   Median :2002-10-25 12:00:00   Median :2002   Median :138.5  
 Mean   :219.48   Mean   :12.001   Gs_4: 13      Rt_4: 50   Mean   :2005-09-17 22:25:44   Mean   :2005   Mean   :170.4  
 3rd Qu.:360.50   3rd Qu.:16.955   Gs_5: 41      Rt_5: 46   3rd Qu.:2015-02-10 06:00:00   3rd Qu.:2015   3rd Qu.:292.0  
 Max.   :798.00   Max.   :91.520   Gs_6:  0      Rt_6: 42   Max.   :2018-01-15 12:00:00   Max.   :2018   Max.   :365.0  
                  NA's   :229                    Rt_7: 19                                                               
      rain             maxt            mint             mean            radn            wind             vp       
 Min.   : 0.000   Min.   : 7.90   Min.   :-4.900   Min.   : 2.55   Min.   : 1.50   Min.   :0.700   Min.   : 5.10  
 1st Qu.: 0.000   1st Qu.:15.32   1st Qu.: 4.375   1st Qu.:10.40   1st Qu.: 9.70   1st Qu.:2.900   1st Qu.:10.00  
 Median : 0.000   Median :19.00   Median : 8.700   Median :13.20   Median :16.14   Median :3.900   Median :11.40  
 Mean   : 0.876   Mean   :19.17   Mean   : 7.853   Mean   :13.49   Mean   :16.73   Mean   :3.997   Mean   :11.77  
 3rd Qu.: 0.000   3rd Qu.:22.23   3rd Qu.:11.300   3rd Qu.:16.50   3rd Qu.:22.62   3rd Qu.:4.900   3rd Qu.:13.72  
 Max.   :31.800   Max.   :33.80   Max.   :20.600   Max.   :26.20   Max.   :33.40   Max.   :9.300   Max.   :22.00  
                                                                                                                  
       Pp              Tb        TTbeta            Tbb       TTbroken           TbF        TTfick      
 Min.   :10.02   Min.   :1   Min.   : 0.128   Min.   :1   Min.   : 1.841   Min.   :1   Min.   : 2.222  
 1st Qu.:12.21   1st Qu.:1   1st Qu.: 2.247   1st Qu.:1   1st Qu.: 6.822   1st Qu.:1   1st Qu.: 8.172  
 Median :14.27   Median :1   Median : 4.118   Median :1   Median : 9.015   Median :1   Median :10.646  
 Mean   :13.93   Mean   :1   Mean   : 5.812   Mean   :1   Mean   : 9.535   Mean   :1   Mean   :10.878  
 3rd Qu.:15.82   3rd Qu.:1   3rd Qu.: 8.085   3rd Qu.:1   3rd Qu.:12.040   3rd Qu.:1   3rd Qu.:13.588  
 Max.   :16.65   Max.   :1   Max.   :23.343   Max.   :1   Max.   :20.258   Max.   :1   Max.   :20.813  
                                                                                                       
                                   ExpName         Experiment.y Water.y   Defoliation.y  SowingDate.y   FD.y    
 Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14   Lincoln1997: 68   dry:  0   HH:  0        No     :175   FD10:  0  
 Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12   Lincoln2000:110   irr:408   LL:408        no     :108   FD2 :  0  
 Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11   Lincoln2001: 15             LS:  0        SD1    : 67   FD5 :408  
 Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11   Lincoln2002:107             SL:  0        SD2    : 25             
 Iversen_8WaterirrGs_5Rt_1             : 10   Lincoln2010:  0             SS:  0        SD3    : 18             
 Iversen_91DefoliationLLGs_2Rt_1       : 10   Lincoln2015:108                           SD4    : 15             
 (Other)                               :340                                             (Other):  0             
 GrowthSeason.y   Rotation.y   Tt_beta_sum        Tt_fick_sum       Tt_broken_sum           Ppm            Tmean       
 Gs_1:160       Rt_1   :112   Min.   :  0.2834   Min.   :   4.022   Min.   :   3.333   Min.   :10.25   Min.   : 7.024  
 Gs_2:140       Rt_2   : 80   1st Qu.: 60.8023   1st Qu.: 165.210   1st Qu.: 142.690   1st Qu.:11.99   1st Qu.:10.974  
 Gs_3: 54       Rt_3   : 59   Median :123.4401   Median : 299.224   Median : 258.979   Median :14.48   Median :13.963  
 Gs_4: 13       Rt_4   : 50   Mean   :157.2934   Mean   : 326.606   Mean   : 283.618   Mean   :13.84   Mean   :13.226  
 Gs_5: 41       Rt_5   : 46   3rd Qu.:218.2569   3rd Qu.: 453.159   3rd Qu.: 395.238   3rd Qu.:15.99   3rd Qu.:15.771  
 Gs_6:  0       Rt_6   : 42   Max.   :722.6708   Max.   :1186.654   Max.   :1051.876   Max.   :16.55   Max.   :19.327  
                (Other): 19                                                                                            
 GrowthRotation   Predicted    
 11     : 60    Min.   :  0.0  
 12     : 38    1st Qu.:103.4  
 21     : 24    Median :222.0  
 26     : 24    Mean   :266.7  
 13     : 23    3rd Qu.:403.0  
 22     : 23    Max.   :858.6  
 (Other):216                   
str(mergedf)
'data.frame':   408 obs. of  54 variables:
 $ Clock.Today   : Date, format: "1997-10-23" "1997-10-28" "1997-11-03" "1997-11-10" ...
 $ Name          : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 16 16 16 16 16 16 16 16 16 16 ...
 $ Variable      : Factor w/ 18 levels "Branch","Fraction",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ ExpUnitCode   : Factor w/ 263 levels "Iversen_121DefoliationHHFDFD5Gs_1Rt_2",..: 152 152 152 152 152 153 153 153 154 154 ...
 $ Collection    : Factor w/ 6 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Experiment.x  : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 2 2 ...
 $ Water.x       : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
 $ Defoliation.x : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
 $ SowingDate.x  : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ FD.x          : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
 $ GrowthSeason.x: int  2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation.x    : int  2 2 2 2 2 3 3 3 4 4 ...
 $ StartDate     : Date, format: "1997-10-07" "1997-10-07" "1997-10-07" "1997-10-07" ...
 $ MidDate       : Date, format: "1997-10-28" "1997-10-28" "1997-10-28" "1997-10-28" ...
 $ FinishDate    : Date, format: "1997-11-19" "1997-11-19" "1997-11-19" "1997-11-19" ...
 $ Interval      : int  16 21 27 34 41 19 28 33 22 30 ...
 $ VariableUnits : Factor w/ 7 levels "%","cm","fractio0l",..: 7 7 7 7 7 7 7 7 7 7 ...
 $ Time          : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
 $ Observed      : num  62.7 146.7 320.7 436.7 564.3 ...
 $ StdDEV        : num  0 0 0 0 0 0 0 0 0 0 ...
 $ GrowthSeason1 : Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation1     : Factor w/ 7 levels "Rt_1","Rt_2",..: 2 2 2 2 2 3 3 3 4 4 ...
 $ Clock.Today1  : POSIXct, format: "1997-10-23 12:00:00" "1997-10-28 12:00:00" "1997-11-03 12:00:00" "1997-11-10 12:00:00" ...
 $ year          : int  1997 1997 1997 1997 1997 1997 1997 1997 1998 1998 ...
 $ day           : int  296 301 307 314 321 343 352 357 15 23 ...
 $ rain          : num  0 0.7 0 0 0 0 0 0 0 0 ...
 $ maxt          : num  15.1 24.3 26 12 18.5 19.4 28 31.9 31.4 25.1 ...
 $ mint          : num  8.8 6.9 5.8 4.7 9.6 8 17 14.1 14.2 15.6 ...
 $ mean          : num  12 15.6 15.9 8.4 14.1 13.7 22.5 23 22.8 20.4 ...
 $ radn          : num  20.2 15.6 26.2 26 22.5 30.7 21.6 19.7 22.1 16 ...
 $ wind          : num  4.3 2.9 5.6 5.7 5.8 4.2 6.1 4.9 7.8 3.9 ...
 $ vp            : num  10.8 13.1 11.5 7.9 10.6 12.3 12.7 16.1 14.2 17.4 ...
 $ Pp            : num  14.6 14.9 15.2 15.5 15.8 ...
 $ Tb            : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTbeta        : num  2.652 8.148 9.211 0.944 4.723 ...
 $ Tbb           : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTbroken      : num  7.87 11.44 11.84 5.3 9.66 ...
 $ TbF           : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTfick        : num  9.49 12.74 13.02 6.4 11.33 ...
 $ ExpName       : Factor w/ 338 levels "Iversen_121DefoliationHHFDFD10Gs_1Rt_2",..: 171 171 171 171 171 172 172 172 173 173 ...
 $ Experiment.y  : Factor w/ 6 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Water.y       : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
 $ Defoliation.y : Factor w/ 5 levels "HH","LL","LS",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ SowingDate.y  : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ FD.y          : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
 $ GrowthSeason.y: Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation.y    : Factor w/ 10 levels "Rt_1","Rt_10",..: 3 3 3 3 3 4 4 4 5 5 ...
 $ Tt_beta_sum   : num  69.7 96.2 141.7 172.5 217.7 ...
 $ Tt_fick_sum   : num  166 220 295 363 447 ...
 $ Tt_broken_sum : num  142 189 256 315 388 ...
 $ Ppm           : num  14.9 14.9 14.9 14.9 14.9 ...
 $ Tmean         : num  13.2 13.2 13.2 13.2 13.2 ...
 $ GrowthRotation: Factor w/ 36 levels "11","12","13",..: 9 9 9 9 9 10 10 10 11 11 ...
 $ Predicted     : num  187 254 355 443 554 ...
mergedf %>%
    dplyr::filter(Collection=="2000_2002")%>%
  ggplot(aes(x=Observed, y= Predicted, 
          colour= factor(Name))) +
  geom_point(size=3)+theme_bw()+
  geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
  geom_abline(intercept = 0, slope = 1) +
  coord_fixed(ratio = 1)+
  ggtitle("Height")+
  facet_grid(GrowthSeason.x~Rotation.x)+
  theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
  theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
  theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

mergedf %>%
    dplyr::filter(Collection=="1997_2001")%>%
  ggplot(aes(x=Observed, y= Predicted, 
          colour= factor(Name))) +
  geom_point(size=3)+theme_bw() +
   geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
  geom_abline(intercept = 0, slope = 1) +
  coord_fixed(ratio = 1) +
  ggtitle("Plant height")  +
  facet_grid(GrowthSeason.x~Rotation.x)+
  theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
  theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
  theme(axis.title.y=element_text(face="bold",colour="black",size = 12))

RMSE

str(mergedf)
'data.frame':   408 obs. of  54 variables:
 $ Clock.Today   : Date, format: "1997-10-23" "1997-10-28" "1997-11-03" "1997-11-10" ...
 $ Name          : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 16 16 16 16 16 16 16 16 16 16 ...
 $ Variable      : Factor w/ 18 levels "Branch","Fraction",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ ExpUnitCode   : Factor w/ 263 levels "Iversen_121DefoliationHHFDFD5Gs_1Rt_2",..: 152 152 152 152 152 153 153 153 154 154 ...
 $ Collection    : Factor w/ 6 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Experiment.x  : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 2 2 ...
 $ Water.x       : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
 $ Defoliation.x : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
 $ SowingDate.x  : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ FD.x          : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
 $ GrowthSeason.x: int  2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation.x    : int  2 2 2 2 2 3 3 3 4 4 ...
 $ StartDate     : Date, format: "1997-10-07" "1997-10-07" "1997-10-07" "1997-10-07" ...
 $ MidDate       : Date, format: "1997-10-28" "1997-10-28" "1997-10-28" "1997-10-28" ...
 $ FinishDate    : Date, format: "1997-11-19" "1997-11-19" "1997-11-19" "1997-11-19" ...
 $ Interval      : int  16 21 27 34 41 19 28 33 22 30 ...
 $ VariableUnits : Factor w/ 7 levels "%","cm","fractio0l",..: 7 7 7 7 7 7 7 7 7 7 ...
 $ Time          : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
 $ Observed      : num  62.7 146.7 320.7 436.7 564.3 ...
 $ StdDEV        : num  0 0 0 0 0 0 0 0 0 0 ...
 $ GrowthSeason1 : Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation1     : Factor w/ 7 levels "Rt_1","Rt_2",..: 2 2 2 2 2 3 3 3 4 4 ...
 $ Clock.Today1  : POSIXct, format: "1997-10-23 12:00:00" "1997-10-28 12:00:00" "1997-11-03 12:00:00" "1997-11-10 12:00:00" ...
 $ year          : int  1997 1997 1997 1997 1997 1997 1997 1997 1998 1998 ...
 $ day           : int  296 301 307 314 321 343 352 357 15 23 ...
 $ rain          : num  0 0.7 0 0 0 0 0 0 0 0 ...
 $ maxt          : num  15.1 24.3 26 12 18.5 19.4 28 31.9 31.4 25.1 ...
 $ mint          : num  8.8 6.9 5.8 4.7 9.6 8 17 14.1 14.2 15.6 ...
 $ mean          : num  12 15.6 15.9 8.4 14.1 13.7 22.5 23 22.8 20.4 ...
 $ radn          : num  20.2 15.6 26.2 26 22.5 30.7 21.6 19.7 22.1 16 ...
 $ wind          : num  4.3 2.9 5.6 5.7 5.8 4.2 6.1 4.9 7.8 3.9 ...
 $ vp            : num  10.8 13.1 11.5 7.9 10.6 12.3 12.7 16.1 14.2 17.4 ...
 $ Pp            : num  14.6 14.9 15.2 15.5 15.8 ...
 $ Tb            : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTbeta        : num  2.652 8.148 9.211 0.944 4.723 ...
 $ Tbb           : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTbroken      : num  7.87 11.44 11.84 5.3 9.66 ...
 $ TbF           : int  1 1 1 1 1 1 1 1 1 1 ...
 $ TTfick        : num  9.49 12.74 13.02 6.4 11.33 ...
 $ ExpName       : Factor w/ 338 levels "Iversen_121DefoliationHHFDFD10Gs_1Rt_2",..: 171 171 171 171 171 172 172 172 173 173 ...
 $ Experiment.y  : Factor w/ 6 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Water.y       : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
 $ Defoliation.y : Factor w/ 5 levels "HH","LL","LS",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ SowingDate.y  : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ FD.y          : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
 $ GrowthSeason.y: Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Rotation.y    : Factor w/ 10 levels "Rt_1","Rt_10",..: 3 3 3 3 3 4 4 4 5 5 ...
 $ Tt_beta_sum   : num  69.7 96.2 141.7 172.5 217.7 ...
 $ Tt_fick_sum   : num  166 220 295 363 447 ...
 $ Tt_broken_sum : num  142 189 256 315 388 ...
 $ Ppm           : num  14.9 14.9 14.9 14.9 14.9 ...
 $ Tmean         : num  13.2 13.2 13.2 13.2 13.2 ...
 $ GrowthRotation: Factor w/ 36 levels "11","12","13",..: 9 9 9 9 9 10 10 10 11 11 ...
 $ Predicted     : num  187 254 355 443 554 ...
mergedf %>%
  group_by(Name) %>%
  summarise(
    n = n(),
    r2 = gauchStats(Predicted,Observed)[5],
  #  rmse = round(rmse(Predicted,Observed),0),
    r_rmse = round(rmse(Predicted,Observed)/mean(Observed)*100,1),
    nse = round(NSE(Predicted,Observed),1),
    sb = gauchStats(Predicted,Observed)[1],
  nu = gauchStats(Predicted,Observed)[2],
  lc = gauchStats(Predicted,Observed)[3]
  ) 
# %>%
#   group_by(Variable,Name) %>%
#   summarise_each(funs(mean))
  
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